train-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from agent import Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device:  cuda:0\n",
      "Using pretrained model: VideoPinballNoFrameskip-v4_10000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/PKlqDg3tW7E0yu+exSooyWgpaIuvvfQiLrOHTlJLv9tHDfY11hRwe7uTxAkdlRGGJEGo8tlRRJCl8vmkR2ivW3I3nu0Jm0pp6Zp8eW/6XX5sr+MX/P2PVE5099f1n3XQr5sM5aT2acRqWcXSQj17AliC+ZF5dc/U968y6u/Eu+bXY3fs09E6tlFEkLBLpIQCnaRhFCwiySEJujKoOfWTWXf5/yf5+R6y8mMUpLu+KqwJffNKM8+8+y7XJrWXRLv/j3lbe+8v05elhj17CIJoWAXSQgN48ug74ozZd/njD1TonJrme65GO7ri8ot//xseXaa3XdZ9xY4vSD+GZT7Zzxv7E0mHPXsIgmhYBdJCAW7SEIo2EUSQhN0FbTmvwXnnjNHj0V1vnIJAPbam1HdW7eujcqnV5d/sm8imbyzLSpf+NcvRuWmGdMB8Pb4eTsT5Gbb8eW5VWpdfSt03fg3gX6CSde0u683s07gB8AS4E3g99392Gj7EJHaKmYY/+/dfZ27rw8f3wlsCTPCbAkfi0idKmUYfyNwbVi+l2BtujtKbM+E4meCtcX9kni98df+qB2AFZ+LE2ZY/a6mX3dyf1aZU/HP8Ne3B1+FOnfFZ/w7ntxbtXY1gkJ7dgf+2cyeM7Nbwrp57t4NEP6f94uRMsKI1IdCe/ar3f2Amc0FHjOznWO+ItQwGWEqafuuqNh6eP15NpTxWvBvwcTmmdktNW5J/SqoZ3f3A+H/h4CHCJaQ7jGz+QDh/+W/7UlEymbMYDezKWY2LVsGfhN4GXiYIBMMKCOMSN0rZBg/D3goyNJMM/A9d3/UzLYBPzSzTwNvAb9XuWY2Nj87FJVNKWUqIjVwNiw1/jC+6654fYSZu3N/Yc6O3LgIYwZ7mPllbZ76XuD6kt5dRKpGl8uKJIQul60CvzpOFDm4MkgO2PSu1bVqzoTUdX2Q7WawMz7h0/FkrVpTmqHp8WcYbinf+v7q2UUSQj17BXX//goAUoPxX+qZYW9z+Mr4ho3T8zJVbVcjy/1Z9X5mY1SefNjD/+Ntsz9/aKybi7wl/n3xMnbH6tlFEkLBLpIQGsZX0IkrG2v42Ai8M75m4ei7a9iQBqSeXSQhFOwiCaFgF0kIBbtIQmiCrgzW3LG/7PvM9PXH5bLvvTFMfuKVqLzm+Wll3Xe6rHtrDOrZRRJCwS6SEBrGl0H6YE+tmzAhZQYG8pZlfNSziyREw/fsZzpTALSvvDiuPHo8Kg4f6a12k0Qiqdmz4gedM6Ji9ve2Msmu8yuoZzezGWb2IzPbaWY7zGyjmXWa2WNmtjv8f2alGysi41foMP6vgEfdfTXBElU7UEYYkYYy5jDezDqA9wB/BODuQ8CQmdVFRphDNwyF/0+P6ub9bE5U7viehvFSO8d+c0VU7nnfUM4zQyM3rrBCevZlwGHg22b2gpl9I1xSWhlhRBpIIcHeDFwO3OPulwGnKGLI7u6b3X29u69PTZsyzmaKSKkKCfYuoMvdt4aPf0QQ/MoII9JAxgx2dz8I7DOzVWHV9cCrKCOMSEMp9Dz7HwP3mdkkYC/wKYI/FHWZEeb48vhvmN+0AYCON05HdfbUi1Vvk0x8vinOpdK3dDIAJy6un+vWCgp2d98O5Es/qowwIg2i4a+gy2fw4njtt0PhhXVDv2yP6uY8Ve0WSRIcWRv/jh3fMFjDluRXP2MMEakoBbtIQijYRRJCwS6SEAp2kYSYkLPx+Rx7V3zf8Mm7g4SAHa/Hz8/6+tPVbpI0sN6b46SSfeEZn7PT49+x8iVaLh/17CIJkZie3drjxYPTYfnM8bbRNhc5rzOz4r47PT+4rqMee/Nc6tlFEkLBLpIQVR3GN502pr449tDZTh6sQmsgk/Ppm+dfEJU9HUy0DB8+XJV2SH1LzQlWPrLmVFSXqUDknFoSfL38T9dsieqOXBdnwnngpctL2r96dpGEqGrPnpnsnFx7Zszt/B/bx9ymHM6siNuy4+4FUbl5fysAS7+knl1gz+eXA5BemHtzy9i/x8VatTLIGfinnfE54WcGz0blB1DPLiIFULCLJMSYwW5mq8xse86/PjO7XUkiRBpLIWvQ7XL3de6+DrgCGAAeQkkiRBpKscP464HX3f3XwI0EySEI//+dcjZMRMqr2Nn4jwHfD8tvSxJhZnmTRDSi9NQMAAMfuarGLSnM6c7wb7bHdfN++gYA6e7qXLMwmuz1Cz0fXBpXhteVTj6aqUGLipf9fWh0Bffs4cqyHwYeKOYNlBFGpD4U07N/AHje3XvCxz1mNj/s1UdNEuHum4HNAK1LF3m+berO9ODc5v4ba9yOAm1asReAv1vyRFT3yn8Pls7+j7v+IKrr+8f5UXnhg78GIN21v6T3bl60EID9H7koquv4re6ofM+q7wFw6aTJUd0fvnktAE/tXlbSe0txivnO/nHiITwoSYRIQyk0P3s7cAPwYE713cANZrY7fO7u8jdPRMql0CQRA8Csc+p6UZKIupAdDi/LMyz+/Pr4porfuH1vVD5+a3BJ8uYD74nq+r8UDMmbfrE97/tkrlkHwLSvxEP/Wxb8CwAzmgaium2n43b89pOfK/BTSKXpCjqRhDD36s2ZTW9f4BtW3zz2hq+9GRUzA3GPkVoenL7xqZPPfUWiNfX2AZDe11X4a6YFt06efO8lUd20l4Mbf4Z37837mtSKoMfuf8ecqG7qv7wKQKa/v+D3bl68KHjNrI6CX5MEw+0tAJyeF98GnhqMT/tNPjD22axf7vw6JwYO5F00Rz27SEIo2EUSoqrD+A7r9KtMc3oilbLVt9DnRzWMF0kyBbtIQijYRRJCwS6SEAp2kYRQsIskhIJdJCEU7CIJoWAXSQgFu0hCKNhFEkLBLpIQhS5L9Xkze8XMXjaz75tZm5ktNbOtYUaYH4Srz4pInSok/dNC4FZgvbu/A0gRrB//58BXw4wwx4BPV7KhIlKaQofxzcBkM2sG2oFu4DrgR+HzyggjUucKyfW2H/gL4C2CID8BPAccd/d0uFkXsLBSjRSR0hUyjJ9JkNdtKbAAmEKQMOJceVfByM0Ic5bBfJuISBUUMox/L/CGux9297MEa8dvAmaEw3qARcCBfC92983uvt7d17fQWpZGi0jxCgn2t4ANZtZuZkawVvyrwOPA74bbKCOMSJ0r5Dv7VoKJuOeBX4Wv2QzcAXzBzPYQJJD4ZgXbKSIl0oKTIhOIFpwUEQW7SFIo2EUSQsEukhBVnaAzs8PAKeBI1d608majz1OvJtJngcI+z0XuPiffE1UNdgAze9bd11f1TStIn6d+TaTPAqV/Hg3jRRJCwS6SELUI9s01eM9K0uepXxPps0CJn6fq39lFpDY0jBdJiKoGu5m938x2mdkeM7uzmu9dKjNbbGaPm9mOcD2+28L6TjN7LFyL77Hw/v+GYWYpM3vBzB4JHzfs2oJmNsPMfmRmO8PjtLGRj0+5136sWrCbWQr4vwQLX1wCfNzMLqnW+5dBGvgTd18DbAA+F7b/TmBLuBbflvBxI7kN2JHzuJHXFvwr4FF3Xw2sJfhcDXl8KrL2o7tX5R+wEfhZzuO7gLuq9f4V+Dz/ANwA7ALmh3XzgV21blsRn2ERQQBcBzwCGMFFG835jlk9/wM6gDcI56Fy6hvy+BAs87YP6CRYA/IR4H2lHJ9qDuOzjc9q2HXrzGwJcBmwFZjn7t0A4f9za9eyon0N+CKQCR/PonHXFlwGHAa+HX4t+YaZTaFBj49XYO3HagZ7vntsG+5UgJlNBX4M3O7ufbVuz3iZ2YeAQ+7+XG51nk0b5Rg1A5cD97j7ZQSXZTfEkD2fUtd+zKeawd4FLM55POq6dfXKzFoIAv0+d38wrO4xs/nh8/OBQ7VqX5GuBj5sZm8C9xMM5b9GgWsL1qEuoMuDlZUgWF3pchr3+JS09mM+1Qz2bcCKcDZxEsFkw8NVfP+ShOvvfRPY4e5/mfPUwwRr8EEDrcXn7ne5+yJ3X0JwLP7V3W+iQdcWdPeDwD4zWxVWZddKbMjjQyXWfqzypMMHgdeA14H/WutJkCLbfg3BkOklYHv474ME33O3ALvD/ztr3dZxfLZrgUfC8jLgGWAP8ADQWuv2FfE51gHPhsfo74GZjXx8gD8DdgIvA98FWks5PrqCTiQhdAWdSEIo2EUSQsEukhAKdpGEULCLJISCXSQhFOwiCaFgF0mI/w8eKrOSBPfgEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "game_name = 'VideoPinballNoFrameskip-v4' # 'Breakout-v0' # 'VideoPinball-v0'\n",
    "device = 'cuda:0'\n",
    "chkpnt_name = 'VideoPinballNoFrameskip-v4_10000'\n",
    "pretrained_name = 'VideoPinballNoFrameskip-v4_10000' # 'VideoPinballNoFrameskip-v4_10000'\n",
    "verbosity = 0\n",
    "\n",
    "agent = Agent(game_name, device, chkpnt_name, pretrained_name, verbosity)\n",
    "\n",
    "plt.imshow(agent.sanity_check_screen())\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eps decay overrided to continue training...\n",
      "Episode  0\n",
      "Episode  1\n",
      "Episode  2\n",
      "Episode  3\n",
      "Episode  4\n",
      "Episode  5\n",
      "Episode  6\n",
      "Episode  7\n",
      "Episode  8\n",
      "Episode  9\n",
      "Episode  10\n",
      "Episode  11\n",
      "Episode  12\n",
      "Episode  13\n",
      "Episode  14\n",
      "Episode  15\n",
      "Episode  16\n",
      "Episode  17\n",
      "Episode  18\n",
      "Episode  19\n",
      "Episode  20\n",
      "Episode  21\n",
      "Episode  22\n",
      "Episode  23\n",
      "Episode  24\n",
      "Episode  25\n",
      "Episode  26\n",
      "Episode  27\n",
      "Episode  28\n",
      "Episode  29\n",
      "Episode  30\n",
      "Episode  31\n",
      "Episode  32\n",
      "Episode  33\n",
      "Episode  34\n",
      "Episode  35\n",
      "Episode  36\n",
      "Episode  37\n",
      "Episode  38\n",
      "Episode  39\n",
      "Episode  40\n",
      "Episode  41\n",
      "Episode  42\n",
      "Episode  43\n",
      "Episode  44\n",
      "Episode  45\n",
      "Episode  46\n",
      "Episode  47\n",
      "Episode  48\n",
      "Episode  49\n",
      "Saving model and metrics ...\n",
      "Episode  50\n",
      "Episode  51\n",
      "Episode  52\n",
      "Episode  53\n",
      "Episode  54\n",
      "Episode  55\n",
      "Episode  56\n",
      "Episode  57\n",
      "Episode  58\n",
      "Episode  59\n",
      "Episode  60\n",
      "Episode  61\n",
      "Episode  62\n",
      "Episode  63\n",
      "Episode  64\n",
      "Episode  65\n",
      "Episode  66\n",
      "Episode  67\n",
      "Episode  68\n",
      "Episode  69\n",
      "Episode  70\n",
      "Episode  71\n",
      "Episode  72\n",
      "Episode  73\n",
      "Episode  74\n",
      "Episode  75\n",
      "Episode  76\n",
      "Episode  77\n",
      "Episode  78\n",
      "Episode  79\n",
      "Episode  80\n",
      "Episode  81\n",
      "Episode  82\n",
      "Episode  83\n",
      "Episode  84\n",
      "Episode  85\n",
      "Episode  86\n",
      "Episode  87\n",
      "Episode  88\n",
      "Episode  89\n",
      "Episode  90\n",
      "Episode  91\n",
      "Episode  92\n",
      "Episode  93\n",
      "Episode  94\n",
      "Episode  95\n",
      "Episode  96\n",
      "Episode  97\n",
      "Episode  98\n",
      "Episode  99\n",
      "Saving model and metrics ...\n",
      "Episode  100\n",
      "Episode  101\n",
      "Episode  102\n",
      "Episode  103\n",
      "Episode  104\n",
      "Episode  105\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-b2f989e4e948>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Eps decay overrided to continue training...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meps_decay\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/mnt/hdd/BITS/NNFL/Design Project/Double-DQN/agent.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                 \u001b[0;31m#Select action using greedy policy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m                 \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m                 \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/BITS/NNFL/Design Project/Double-DQN/agent.py\u001b[0m in \u001b[0;36mselect_action\u001b[0;34m(self, steps, state)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;31m#exploitation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m             \u001b[0;31m#use primary_network to estimate q-values of actions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m255.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if pretrained_name != None:\n",
    "    print('Eps decay overrided to continue training...')\n",
    "    agent.eps_decay = 1\n",
    "metrics = agent.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 0 :\n",
      "Step -  648\n",
      "Cumulative steps -  648\n",
      "Reward -  24.0\n",
      "Loss -  0.02364618331193924\n",
      "\n",
      "\n",
      "Episode 1 :\n",
      "Step -  3172\n",
      "Cumulative steps -  3820\n",
      "Reward -  153.0\n",
      "Loss -  0.06639689207077026\n",
      "\n",
      "\n",
      "Episode 2 :\n",
      "Step -  2051\n",
      "Cumulative steps -  5871\n",
      "Reward -  90.0\n",
      "Loss -  0.06099477782845497\n",
      "\n",
      "\n",
      "Episode 3 :\n",
      "Step -  1180\n",
      "Cumulative steps -  7051\n",
      "Reward -  67.0\n",
      "Loss -  0.08595005422830582\n",
      "\n",
      "\n",
      "Episode 4 :\n",
      "Step -  3808\n",
      "Cumulative steps -  10859\n",
      "Reward -  196.0\n",
      "Loss -  0.04402824863791466\n",
      "\n",
      "\n",
      "Episode 5 :\n",
      "Step -  1614\n",
      "Cumulative steps -  12473\n",
      "Reward -  87.0\n",
      "Loss -  0.056510038673877716\n",
      "\n",
      "\n",
      "Episode 6 :\n",
      "Step -  1951\n",
      "Cumulative steps -  14424\n",
      "Reward -  70.0\n",
      "Loss -  0.03518342226743698\n",
      "\n",
      "\n",
      "Episode 7 :\n",
      "Step -  2115\n",
      "Cumulative steps -  16539\n",
      "Reward -  109.0\n",
      "Loss -  0.12126432359218597\n",
      "\n",
      "\n",
      "Episode 8 :\n",
      "Step -  3227\n",
      "Cumulative steps -  19766\n",
      "Reward -  153.0\n",
      "Loss -  0.15686196088790894\n",
      "\n",
      "\n",
      "Episode 9 :\n",
      "Step -  4038\n",
      "Cumulative steps -  23804\n",
      "Reward -  255.0\n",
      "Loss -  0.10220281034708023\n",
      "\n",
      "\n",
      "Episode 10 :\n",
      "Step -  2284\n",
      "Cumulative steps -  26088\n",
      "Reward -  129.0\n",
      "Loss -  0.036948177963495255\n",
      "\n",
      "\n",
      "Episode 11 :\n",
      "Step -  3591\n",
      "Cumulative steps -  29679\n",
      "Reward -  201.0\n",
      "Loss -  0.08390621840953827\n",
      "\n",
      "\n",
      "Episode 12 :\n",
      "Step -  1162\n",
      "Cumulative steps -  30841\n",
      "Reward -  53.0\n",
      "Loss -  0.012045223265886307\n",
      "\n",
      "\n",
      "Episode 13 :\n",
      "Step -  6196\n",
      "Cumulative steps -  37037\n",
      "Reward -  326.0\n",
      "Loss -  0.015645239502191544\n",
      "\n",
      "\n",
      "Episode 14 :\n",
      "Step -  638\n",
      "Cumulative steps -  37675\n",
      "Reward -  28.0\n",
      "Loss -  0.06829565763473511\n",
      "\n",
      "\n",
      "Episode 15 :\n",
      "Step -  3913\n",
      "Cumulative steps -  41588\n",
      "Reward -  178.0\n",
      "Loss -  0.05602792277932167\n",
      "\n",
      "\n",
      "Episode 16 :\n",
      "Step -  2146\n",
      "Cumulative steps -  43734\n",
      "Reward -  111.0\n",
      "Loss -  0.0036403744015842676\n",
      "\n",
      "\n",
      "Episode 17 :\n",
      "Step -  1871\n",
      "Cumulative steps -  45605\n",
      "Reward -  71.0\n",
      "Loss -  0.019414082169532776\n",
      "\n",
      "\n",
      "Episode 18 :\n",
      "Step -  1779\n",
      "Cumulative steps -  47384\n",
      "Reward -  78.0\n",
      "Loss -  0.05846232920885086\n",
      "\n",
      "\n",
      "Episode 19 :\n",
      "Step -  2540\n",
      "Cumulative steps -  49924\n",
      "Reward -  144.0\n",
      "Loss -  0.03388238325715065\n",
      "\n",
      "\n",
      "Episode 20 :\n",
      "Step -  2499\n",
      "Cumulative steps -  52423\n",
      "Reward -  113.0\n",
      "Loss -  0.04539273679256439\n",
      "\n",
      "\n",
      "Episode 21 :\n",
      "Step -  1357\n",
      "Cumulative steps -  53780\n",
      "Reward -  41.0\n",
      "Loss -  0.08288535475730896\n",
      "\n",
      "\n",
      "Episode 22 :\n",
      "Step -  5374\n",
      "Cumulative steps -  59154\n",
      "Reward -  298.0\n",
      "Loss -  0.04008840024471283\n",
      "\n",
      "\n",
      "Episode 23 :\n",
      "Step -  1105\n",
      "Cumulative steps -  60259\n",
      "Reward -  53.0\n",
      "Loss -  0.012057959102094173\n",
      "\n",
      "\n",
      "Episode 24 :\n",
      "Step -  4552\n",
      "Cumulative steps -  64811\n",
      "Reward -  253.0\n",
      "Loss -  0.03257828950881958\n",
      "\n",
      "\n",
      "Episode 25 :\n",
      "Step -  3440\n",
      "Cumulative steps -  68251\n",
      "Reward -  184.0\n",
      "Loss -  0.009260458871722221\n",
      "\n",
      "\n",
      "Episode 26 :\n",
      "Step -  4752\n",
      "Cumulative steps -  73003\n",
      "Reward -  248.0\n",
      "Loss -  0.09409211575984955\n",
      "\n",
      "\n",
      "Episode 27 :\n",
      "Step -  806\n",
      "Cumulative steps -  73809\n",
      "Reward -  40.0\n",
      "Loss -  0.014857781119644642\n",
      "\n",
      "\n",
      "Episode 28 :\n",
      "Step -  798\n",
      "Cumulative steps -  74607\n",
      "Reward -  45.0\n",
      "Loss -  0.08996986597776413\n",
      "\n",
      "\n",
      "Episode 29 :\n",
      "Step -  2036\n",
      "Cumulative steps -  76643\n",
      "Reward -  95.0\n",
      "Loss -  0.04674903303384781\n",
      "\n",
      "\n",
      "Episode 30 :\n",
      "Step -  983\n",
      "Cumulative steps -  77626\n",
      "Reward -  63.0\n",
      "Loss -  0.10259734094142914\n",
      "\n",
      "\n",
      "Episode 31 :\n",
      "Step -  1151\n",
      "Cumulative steps -  78777\n",
      "Reward -  59.0\n",
      "Loss -  0.05855386704206467\n",
      "\n",
      "\n",
      "Episode 32 :\n",
      "Step -  7104\n",
      "Cumulative steps -  85881\n",
      "Reward -  404.0\n",
      "Loss -  0.05004511773586273\n",
      "\n",
      "\n",
      "Episode 33 :\n",
      "Step -  2792\n",
      "Cumulative steps -  88673\n",
      "Reward -  140.0\n",
      "Loss -  0.038730476051568985\n",
      "\n",
      "\n",
      "Episode 34 :\n",
      "Step -  2901\n",
      "Cumulative steps -  91574\n",
      "Reward -  161.0\n",
      "Loss -  0.012955361977219582\n",
      "\n",
      "\n",
      "Episode 35 :\n",
      "Step -  2913\n",
      "Cumulative steps -  94487\n",
      "Reward -  150.0\n",
      "Loss -  0.04948461800813675\n",
      "\n",
      "\n",
      "Episode 36 :\n",
      "Step -  2611\n",
      "Cumulative steps -  97098\n",
      "Reward -  133.0\n",
      "Loss -  0.0516958050429821\n",
      "\n",
      "\n",
      "Episode 37 :\n",
      "Step -  710\n",
      "Cumulative steps -  97808\n",
      "Reward -  23.0\n",
      "Loss -  0.023924781009554863\n",
      "\n",
      "\n",
      "Episode 38 :\n",
      "Step -  3053\n",
      "Cumulative steps -  100861\n",
      "Reward -  170.0\n",
      "Loss -  0.02317698672413826\n",
      "\n",
      "\n",
      "Episode 39 :\n",
      "Step -  1645\n",
      "Cumulative steps -  102506\n",
      "Reward -  80.0\n",
      "Loss -  0.04407630115747452\n",
      "\n",
      "\n",
      "Episode 40 :\n",
      "Step -  1268\n",
      "Cumulative steps -  103774\n",
      "Reward -  60.0\n",
      "Loss -  0.033417124301195145\n",
      "\n",
      "\n",
      "Episode 41 :\n",
      "Step -  681\n",
      "Cumulative steps -  104455\n",
      "Reward -  29.0\n",
      "Loss -  0.08707880228757858\n",
      "\n",
      "\n",
      "Episode 42 :\n",
      "Step -  3816\n",
      "Cumulative steps -  108271\n",
      "Reward -  209.0\n",
      "Loss -  0.054555609822273254\n",
      "\n",
      "\n",
      "Episode 43 :\n",
      "Step -  4952\n",
      "Cumulative steps -  113223\n",
      "Reward -  286.0\n",
      "Loss -  0.033720891922712326\n",
      "\n",
      "\n",
      "Episode 44 :\n",
      "Step -  2439\n",
      "Cumulative steps -  115662\n",
      "Reward -  125.0\n",
      "Loss -  0.0362522229552269\n",
      "\n",
      "\n",
      "Episode 45 :\n",
      "Step -  1473\n",
      "Cumulative steps -  117135\n",
      "Reward -  56.0\n",
      "Loss -  0.04300331324338913\n",
      "\n",
      "\n",
      "Episode 46 :\n",
      "Step -  5545\n",
      "Cumulative steps -  122680\n",
      "Reward -  297.0\n",
      "Loss -  0.05709084868431091\n",
      "\n",
      "\n",
      "Episode 47 :\n",
      "Step -  1700\n",
      "Cumulative steps -  124380\n",
      "Reward -  81.0\n",
      "Loss -  0.05474209040403366\n",
      "\n",
      "\n",
      "Episode 48 :\n",
      "Step -  2626\n",
      "Cumulative steps -  127006\n",
      "Reward -  136.0\n",
      "Loss -  0.034843020141124725\n",
      "\n",
      "\n",
      "Episode 49 :\n",
      "Step -  2267\n",
      "Cumulative steps -  129273\n",
      "Reward -  122.0\n",
      "Loss -  0.03773641213774681\n",
      "\n",
      "\n",
      "Episode 50 :\n",
      "Step -  4623\n",
      "Cumulative steps -  133896\n",
      "Reward -  300.0\n",
      "Loss -  0.053016647696495056\n",
      "\n",
      "\n",
      "Episode 51 :\n",
      "Step -  3088\n",
      "Cumulative steps -  136984\n",
      "Reward -  176.0\n",
      "Loss -  0.09581531584262848\n",
      "\n",
      "\n",
      "Episode 52 :\n",
      "Step -  1613\n",
      "Cumulative steps -  138597\n",
      "Reward -  69.0\n",
      "Loss -  0.07481538504362106\n",
      "\n",
      "\n",
      "Episode 53 :\n",
      "Step -  890\n",
      "Cumulative steps -  139487\n",
      "Reward -  39.0\n",
      "Loss -  0.1139301061630249\n",
      "\n",
      "\n",
      "Episode 54 :\n",
      "Step -  996\n",
      "Cumulative steps -  140483\n",
      "Reward -  44.0\n",
      "Loss -  0.038746196776628494\n",
      "\n",
      "\n",
      "Episode 55 :\n",
      "Step -  1785\n",
      "Cumulative steps -  142268\n",
      "Reward -  83.0\n",
      "Loss -  0.1255788654088974\n",
      "\n",
      "\n",
      "Episode 56 :\n",
      "Step -  482\n",
      "Cumulative steps -  142750\n",
      "Reward -  18.0\n",
      "Loss -  0.07760797441005707\n",
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      "Episode 57 :\n",
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      "Episode 66 :\n",
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      "Episode 72 :\n",
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      "Episode 76 :\n",
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      "Episode 79 :\n",
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      "Episode 80 :\n",
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      "Episode 81 :\n",
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      "Episode 82 :\n",
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      "Episode 83 :\n",
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      "Episode 84 :\n",
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      "Episode 85 :\n",
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      "Episode 86 :\n",
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      "Reward -  187.0\n",
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      "Episode 87 :\n",
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      "\n",
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      "Episode 88 :\n",
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      "Reward -  325.0\n",
      "Loss -  0.09377492219209671\n",
      "\n",
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      "Episode 89 :\n",
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      "Reward -  63.0\n",
      "Loss -  0.15559320151805878\n",
      "\n",
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      "Episode 90 :\n",
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      "Reward -  79.0\n",
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      "\n",
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      "Episode 91 :\n",
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      "Reward -  131.0\n",
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      "Episode 92 :\n",
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      "Episode 93 :\n",
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      "Reward -  189.0\n",
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      "Episode 94 :\n",
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      "Episode 95 :\n",
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      "Episode 96 :\n",
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      "Episode 97 :\n",
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      "Episode 98 :\n",
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      "Episode 99 :\n",
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      "Reward -  169.0\n",
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      "Episode 100 :\n",
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      "Reward -  276.0\n",
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      "Episode 101 :\n",
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      "Reward -  98.0\n",
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      "Episode 102 :\n",
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      "Reward -  69.0\n",
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      "Episode 103 :\n",
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      "Reward -  216.0\n",
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      "Episode 104 :\n",
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      "Episode 105 :\n",
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      "Episode 106 :\n",
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      "Episode 107 :\n",
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      "Reward -  61.0\n",
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      "Episode 108 :\n",
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      "Episode 109 :\n",
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      "Reward -  94.0\n",
      "Loss -  0.033828746527433395\n",
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      "Episode 110 :\n",
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      "Reward -  148.0\n",
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      "Episode 111 :\n",
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      "Reward -  170.0\n",
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      "Episode 112 :\n",
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      "Reward -  184.0\n",
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      "Episode 113 :\n",
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      "Episode 114 :\n",
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      "Episode 116 :\n",
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      "Loss -  0.04529118537902832\n",
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      "Episode 117 :\n",
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      "Episode 118 :\n",
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      "Reward -  126.0\n",
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      "Episode 119 :\n",
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      "Episode 120 :\n",
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    },
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     "output_type": "stream",
     "text": [
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      "Episode 349 :\n",
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      "Episode 366 :\n",
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      "Episode 367 :\n",
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      "Episode 368 :\n",
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      "Episode 369 :\n",
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      "Episode 370 :\n",
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      "Episode 372 :\n",
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      "Episode 374 :\n",
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      "Episode 375 :\n",
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      "Episode 376 :\n",
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      "Episode 377 :\n",
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      "Episode 378 :\n",
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      "Episode 379 :\n",
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      "Reward -  118.0\n",
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      "Episode 380 :\n",
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      "Episode 381 :\n",
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      "Episode 382 :\n",
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      "Episode 383 :\n",
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      "Reward -  86.0\n",
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      "Episode 384 :\n",
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      "Episode 385 :\n",
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      "Episode 386 :\n",
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      "Episode 387 :\n",
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      "Episode 389 :\n",
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      "Reward -  250.0\n",
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      "Reward -  78.0\n",
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      "Reward -  63.0\n",
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      "Reward -  77.0\n",
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      "Reward -  67.0\n",
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      "Reward -  192.0\n",
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      "Episode 400 :\n",
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      "Reward -  187.0\n",
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      "Reward -  110.0\n",
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      "Reward -  189.0\n",
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      "Reward -  215.0\n",
      "Loss -  0.02401037886738777\n",
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      "Episode 407 :\n",
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      "Reward -  232.0\n",
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      "Episode 446 :\n",
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      "Episode 447 :\n",
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      "Episode 448 :\n",
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      "Episode 450 :\n",
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      "Reward -  51.0\n",
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      "Reward -  121.0\n",
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      "Episode 459 :\n",
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      "Episode 460 :\n",
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      "Episode 464 :\n",
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      "Episode 466 :\n",
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      "Episode 467 :\n",
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      "Reward -  46.0\n",
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      "Episode 468 :\n",
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      "\n",
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      "Episode 469 :\n",
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      "\n",
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      "Episode 470 :\n",
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      "Reward -  134.0\n",
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      "\n",
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      "Episode 471 :\n",
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      "Reward -  75.0\n",
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      "\n",
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      "Episode 472 :\n",
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      "Reward -  280.0\n",
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      "\n",
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      "Episode 473 :\n",
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      "Reward -  111.0\n",
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      "\n",
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      "Episode 474 :\n",
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      "Reward -  232.0\n",
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      "Episode 475 :\n",
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      "Reward -  98.0\n",
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      "\n",
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      "Episode 476 :\n",
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      "Reward -  128.0\n",
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      "Episode 477 :\n",
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      "Episode 478 :\n",
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      "Reward -  343.0\n",
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      "\n",
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      "Episode 479 :\n",
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      "Reward -  39.0\n",
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      "\n",
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      "Episode 480 :\n",
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      "Reward -  149.0\n",
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      "\n",
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      "Episode 481 :\n",
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      "Reward -  62.0\n",
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      "\n",
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      "Episode 482 :\n",
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      "Reward -  204.0\n",
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      "\n",
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      "Episode 483 :\n",
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      "Reward -  186.0\n",
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      "\n",
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      "Episode 484 :\n",
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      "Reward -  133.0\n",
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      "\n",
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      "Episode 485 :\n",
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      "Reward -  9.0\n",
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      "\n",
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      "Episode 486 :\n",
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      "Reward -  86.0\n",
      "Loss -  0.035114433616399765\n",
      "\n",
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      "Episode 487 :\n",
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      "Reward -  129.0\n",
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      "\n",
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      "Episode 488 :\n",
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      "Reward -  173.0\n",
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      "\n",
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      "Episode 489 :\n",
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      "Reward -  207.0\n",
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      "\n",
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      "Episode 490 :\n",
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      "Reward -  165.0\n",
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      "\n",
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      "Episode 491 :\n",
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      "Reward -  226.0\n",
      "Loss -  0.04510677233338356\n",
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      "Episode 492 :\n",
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      "Reward -  163.0\n",
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      "Reward -  290.0\n",
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      "Reward -  290.0\n",
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      "\n",
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      "Episode 495 :\n",
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      "Reward -  220.0\n",
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      "Episode 496 :\n",
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      "Reward -  294.0\n",
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      "Episode 497 :\n",
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      "Reward -  88.0\n",
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      "Episode 498 :\n",
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      "Reward -  69.0\n",
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      "Episode 499 :\n",
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      "Reward -  139.0\n",
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      "\n",
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      "Episode 500 :\n",
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      "Reward -  219.0\n",
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      "\n",
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      "Reward -  88.0\n",
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      "Loss -  0.027281628921628\n",
      "\n",
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      "Episode 504 :\n",
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      "Reward -  128.0\n",
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      "\n",
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      "Episode 505 :\n",
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      "Reward -  215.0\n",
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      "Episode 506 :\n",
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      "Reward -  44.0\n",
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      "Episode 507 :\n",
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      "Reward -  139.0\n",
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      "\n",
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      "Episode 508 :\n",
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      "Reward -  190.0\n",
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      "\n",
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      "Episode 509 :\n",
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      "Reward -  82.0\n",
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      "\n",
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      "Episode 510 :\n",
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      "Reward -  108.0\n",
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      "Episode 511 :\n",
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      "Reward -  57.0\n",
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      "Episode 512 :\n",
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      "Reward -  41.0\n",
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      "Episode 513 :\n",
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      "Reward -  86.0\n",
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      "Episode 514 :\n",
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      "Episode 515 :\n",
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      "Reward -  188.0\n",
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      "Episode 517 :\n",
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      "Reward -  107.0\n",
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      "Episode 518 :\n",
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      "Episode 519 :\n",
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      "Episode 522 :\n",
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      "Episode 524 :\n",
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      "\n",
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      "Episode 543 :\n",
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      "\n",
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      "Episode 544 :\n",
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      "Reward -  238.0\n",
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      "\n",
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      "Episode 545 :\n",
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      "Reward -  189.0\n",
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      "\n",
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      "Episode 546 :\n",
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      "Reward -  48.0\n",
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      "\n",
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      "Episode 547 :\n",
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      "Reward -  221.0\n",
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      "\n",
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      "Episode 548 :\n",
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      "\n",
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      "Episode 549 :\n",
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      "Reward -  282.0\n",
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      "\n",
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      "Episode 550 :\n",
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      "Reward -  132.0\n",
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      "\n",
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      "Episode 551 :\n",
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      "Reward -  111.0\n",
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      "\n",
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      "Episode 552 :\n",
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      "Reward -  131.0\n",
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      "Episode 553 :\n",
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      "Episode 554 :\n",
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      "Reward -  102.0\n",
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      "\n",
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      "Episode 555 :\n",
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      "Reward -  192.0\n",
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      "\n",
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      "Episode 556 :\n",
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      "Reward -  83.0\n",
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      "\n",
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      "Episode 557 :\n",
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      "Episode 558 :\n",
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      "\n",
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      "Episode 559 :\n",
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      "Reward -  82.0\n",
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      "\n",
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      "Episode 560 :\n",
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      "Reward -  185.0\n",
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      "\n",
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      "Episode 561 :\n",
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      "Reward -  22.0\n",
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      "\n",
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      "Episode 562 :\n",
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      "Reward -  126.0\n",
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      "\n",
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      "Episode 563 :\n",
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      "\n",
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      "Episode 564 :\n",
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      "Reward -  211.0\n",
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      "\n",
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      "Episode 565 :\n",
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      "Reward -  95.0\n",
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      "Episode 566 :\n",
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      "Reward -  241.0\n",
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      "Episode 567 :\n",
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      "Reward -  203.0\n",
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      "\n",
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      "Episode 568 :\n",
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      "Reward -  301.0\n",
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      "\n",
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      "Episode 569 :\n",
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      "Reward -  201.0\n",
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      "\n",
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      "Episode 570 :\n",
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      "Reward -  76.0\n",
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      "\n",
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      "Episode 571 :\n",
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      "Reward -  217.0\n",
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      "Episode 573 :\n",
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      "\n",
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      "Episode 574 :\n",
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      "\n",
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      "Episode 575 :\n",
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      "Reward -  206.0\n",
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      "\n",
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      "Episode 576 :\n",
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      "Reward -  208.0\n",
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      "\n",
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      "Episode 577 :\n",
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      "\n",
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      "Episode 578 :\n",
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      "Reward -  134.0\n",
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      "Episode 579 :\n",
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      "Reward -  134.0\n",
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      "\n",
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      "Episode 580 :\n",
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      "Reward -  25.0\n",
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      "Episode 582 :\n",
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      "Reward -  49.0\n",
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      "Episode 584 :\n",
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      "Reward -  176.0\n",
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      "Episode 585 :\n",
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      "Reward -  136.0\n",
      "Loss -  0.07487811893224716\n",
      "\n",
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      "Episode 586 :\n",
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      "Reward -  119.0\n",
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      "\n",
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      "Episode 587 :\n",
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      "Reward -  122.0\n",
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      "Episode 588 :\n",
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      "Reward -  258.0\n",
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      "Episode 589 :\n",
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      "Reward -  300.0\n",
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      "Episode 590 :\n",
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      "Reward -  61.0\n",
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      "Reward -  252.0\n",
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      "Episode 592 :\n",
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      "Reward -  59.0\n",
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      "Episode 594 :\n",
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      "Reward -  176.0\n",
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      "Episode 595 :\n",
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      "Episode 596 :\n",
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      "Reward -  178.0\n",
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      "Episode 598 :\n",
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      "Reward -  279.0\n",
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      "\n",
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      "Episode 599 :\n",
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      "Reward -  204.0\n",
      "Loss -  0.04870247095823288\n",
      "\n",
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      "Episode 600 :\n",
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      "Reward -  119.0\n",
      "Loss -  0.07683123648166656\n",
      "\n",
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      "Episode 601 :\n",
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      "Reward -  128.0\n",
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      "\n",
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      "Episode 602 :\n",
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      "Reward -  6.0\n",
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      "Episode 603 :\n",
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      "Loss -  0.03501066938042641\n",
      "\n",
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      "Episode 604 :\n",
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      "Reward -  87.0\n",
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      "\n",
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      "Episode 605 :\n",
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      "Reward -  98.0\n",
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      "Episode 606 :\n",
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      "Reward -  107.0\n",
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      "\n",
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      "Episode 607 :\n",
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      "Reward -  124.0\n",
      "Loss -  0.05744456872344017\n",
      "\n",
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      "Episode 608 :\n",
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      "Reward -  198.0\n",
      "Loss -  0.030618229880928993\n",
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      "Episode 609 :\n",
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      "Reward -  65.0\n",
      "Loss -  0.03180161118507385\n",
      "\n",
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      "Episode 610 :\n",
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      "Reward -  98.0\n",
      "Loss -  0.03960117697715759\n",
      "\n",
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      "Episode 611 :\n",
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      "Reward -  274.0\n",
      "Loss -  0.060092344880104065\n",
      "\n",
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      "Episode 612 :\n",
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      "Reward -  317.0\n",
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      "\n",
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      "Episode 613 :\n",
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      "Reward -  147.0\n",
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      "\n",
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      "Episode 614 :\n",
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      "Reward -  285.0\n",
      "Loss -  0.047576017677783966\n",
      "\n",
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      "Episode 615 :\n",
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      "Reward -  142.0\n",
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      "\n",
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      "Episode 616 :\n",
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      "Reward -  114.0\n",
      "Loss -  0.03752096742391586\n",
      "\n",
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      "Episode 617 :\n",
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      "\n",
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      "Episode 618 :\n",
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      "Reward -  83.0\n",
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      "\n",
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      "Episode 619 :\n",
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      "Episode 620 :\n",
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      "Episode 621 :\n",
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      "Reward -  68.0\n",
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      "Episode 623 :\n",
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      "Episode 624 :\n",
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      "Episode 625 :\n",
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      "Episode 630 :\n",
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      "Reward -  168.0\n",
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      "\n",
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     ]
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     "output_type": "stream",
     "text": [
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      "Episode 660 :\n",
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      "Loss -  0.051555052399635315\n",
      "\n",
      "\n",
      "Episode 735 :\n",
      "Step -  3763\n",
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      "Reward -  222.0\n",
      "Loss -  0.0311921164393425\n",
      "\n",
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      "Episode 736 :\n",
      "Step -  3018\n",
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      "Reward -  226.0\n",
      "Loss -  0.0523114949464798\n",
      "\n",
      "\n",
      "Episode 737 :\n",
      "Step -  1478\n",
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      "Reward -  90.0\n",
      "Loss -  0.028626805171370506\n",
      "\n",
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      "Episode 738 :\n",
      "Step -  720\n",
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      "Reward -  13.0\n",
      "Loss -  0.021589817479252815\n",
      "\n",
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      "Episode 739 :\n",
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      "Reward -  39.0\n",
      "Loss -  0.10746603459119797\n",
      "\n",
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      "Episode 740 :\n",
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      "Reward -  428.0\n",
      "Loss -  0.09212607145309448\n",
      "\n",
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      "Episode 741 :\n",
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      "Reward -  238.0\n",
      "Loss -  0.05927668511867523\n",
      "\n",
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      "Episode 742 :\n",
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      "Reward -  264.0\n",
      "Loss -  0.06493853777647018\n",
      "\n",
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      "Episode 743 :\n",
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      "Reward -  206.0\n",
      "Loss -  0.06656062602996826\n",
      "\n",
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      "Episode 744 :\n",
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      "Reward -  122.0\n",
      "Loss -  0.035837940871715546\n",
      "\n",
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      "Episode 745 :\n",
      "Step -  1009\n",
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      "Reward -  36.0\n",
      "Loss -  0.03788454085588455\n",
      "\n",
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      "Episode 746 :\n",
      "Step -  2185\n",
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      "Reward -  113.0\n",
      "Loss -  0.0411812849342823\n",
      "\n",
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      "Episode 747 :\n",
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      "Reward -  249.0\n",
      "Loss -  0.04268968850374222\n",
      "\n",
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      "Episode 748 :\n",
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      "Reward -  484.0\n",
      "Loss -  0.07935086637735367\n",
      "\n",
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      "Episode 749 :\n",
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      "Reward -  97.0\n",
      "Loss -  0.0719236508011818\n",
      "\n",
      "\n",
      "Episode 750 :\n",
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      "Reward -  14.0\n",
      "Loss -  0.08335191011428833\n",
      "\n",
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      "Episode 751 :\n",
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      "Reward -  146.0\n",
      "Loss -  0.060245614498853683\n",
      "\n",
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      "Episode 752 :\n",
      "Step -  2175\n",
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      "Reward -  174.0\n",
      "Loss -  0.032328881323337555\n",
      "\n",
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      "Episode 753 :\n",
      "Step -  3930\n",
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      "Reward -  259.0\n",
      "Loss -  0.0675777941942215\n",
      "\n",
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      "Episode 754 :\n",
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      "Reward -  115.0\n",
      "Loss -  0.13452059030532837\n",
      "\n",
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      "Episode 755 :\n",
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      "Reward -  277.0\n",
      "Loss -  0.07454738765954971\n",
      "\n",
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      "Episode 756 :\n",
      "Step -  2253\n",
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      "Reward -  105.0\n",
      "Loss -  0.05778346210718155\n",
      "\n",
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      "Episode 757 :\n",
      "Step -  5392\n",
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      "Reward -  371.0\n",
      "Loss -  0.06694724410772324\n",
      "\n",
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      "Episode 758 :\n",
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      "Reward -  196.0\n",
      "Loss -  0.025476401671767235\n",
      "\n",
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      "Episode 759 :\n",
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      "Reward -  265.0\n",
      "Loss -  0.062384746968746185\n",
      "\n",
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      "Episode 760 :\n",
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      "Reward -  378.0\n",
      "Loss -  0.07143302261829376\n",
      "\n",
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      "Episode 761 :\n",
      "Step -  2806\n",
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      "Reward -  146.0\n",
      "Loss -  0.09170942008495331\n",
      "\n",
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      "Episode 762 :\n",
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      "Reward -  36.0\n",
      "Loss -  0.05432360619306564\n",
      "\n",
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      "Episode 763 :\n",
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      "Reward -  335.0\n",
      "Loss -  0.0661868005990982\n",
      "\n",
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      "Episode 764 :\n",
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      "Reward -  158.0\n",
      "Loss -  0.031382687389850616\n",
      "\n",
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      "Episode 765 :\n",
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      "Reward -  160.0\n",
      "Loss -  0.05352549999952316\n",
      "\n",
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      "Episode 766 :\n",
      "Step -  4028\n",
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      "Reward -  260.0\n",
      "Loss -  0.43799880146980286\n",
      "\n",
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      "Episode 767 :\n",
      "Step -  2431\n",
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      "Reward -  128.0\n",
      "Loss -  0.05879686772823334\n",
      "\n",
      "\n",
      "Episode 768 :\n",
      "Step -  2513\n",
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      "Reward -  36.0\n",
      "Loss -  0.03134141117334366\n",
      "\n",
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      "Episode 769 :\n",
      "Step -  1812\n",
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      "Reward -  87.0\n",
      "Loss -  0.0299992673099041\n",
      "\n",
      "\n",
      "Episode 770 :\n",
      "Step -  4390\n",
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      "Reward -  254.0\n",
      "Loss -  0.09342675656080246\n",
      "\n",
      "\n",
      "Episode 771 :\n",
      "Step -  2255\n",
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      "Reward -  144.0\n",
      "Loss -  0.050831276923418045\n",
      "\n",
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      "Episode 772 :\n",
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      "Reward -  104.0\n",
      "Loss -  0.03758963569998741\n",
      "\n",
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      "Episode 773 :\n",
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      "Reward -  103.0\n",
      "Loss -  0.056966423988342285\n",
      "\n",
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      "Episode 774 :\n",
      "Step -  2038\n",
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      "Reward -  128.0\n",
      "Loss -  0.0712718814611435\n",
      "\n",
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      "Episode 775 :\n",
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      "Reward -  290.0\n",
      "Loss -  0.04193982854485512\n",
      "\n",
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      "Episode 776 :\n",
      "Step -  1260\n",
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      "Reward -  47.0\n",
      "Loss -  0.05799798294901848\n",
      "\n",
      "\n",
      "Episode 777 :\n",
      "Step -  4770\n",
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      "Reward -  322.0\n",
      "Loss -  0.08166301250457764\n",
      "\n",
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      "Episode 778 :\n",
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      "Reward -  130.0\n",
      "Loss -  0.05161048844456673\n",
      "\n",
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      "Episode 779 :\n",
      "Step -  2028\n",
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      "Reward -  121.0\n",
      "Loss -  0.03666776791214943\n",
      "\n",
      "\n",
      "Episode 780 :\n",
      "Step -  1991\n",
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      "Reward -  115.0\n",
      "Loss -  0.24699769914150238\n",
      "\n",
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      "Episode 781 :\n",
      "Step -  4510\n",
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      "Reward -  315.0\n",
      "Loss -  0.06340458244085312\n",
      "\n",
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      "Episode 782 :\n",
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      "Reward -  121.0\n",
      "Loss -  0.04627380892634392\n",
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      "Episode 783 :\n",
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      "Reward -  166.0\n",
      "Loss -  0.07150866836309433\n",
      "\n",
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      "Episode 784 :\n",
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      "Reward -  21.0\n",
      "Loss -  0.0493946447968483\n",
      "\n",
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      "Episode 785 :\n",
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      "Reward -  121.0\n",
      "Loss -  0.07785002887248993\n",
      "\n",
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      "Episode 786 :\n",
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      "Reward -  71.0\n",
      "Loss -  0.06675371527671814\n",
      "\n",
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      "Episode 787 :\n",
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      "Reward -  231.0\n",
      "Loss -  0.023431073874235153\n",
      "\n",
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      "Episode 788 :\n",
      "Step -  3200\n",
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      "Reward -  184.0\n",
      "Loss -  0.3452577590942383\n",
      "\n",
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      "Episode 789 :\n",
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      "Reward -  47.0\n",
      "Loss -  0.05313795804977417\n",
      "\n",
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      "Episode 790 :\n",
      "Step -  2441\n",
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      "Reward -  176.0\n",
      "Loss -  0.04625551775097847\n",
      "\n",
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      "Episode 791 :\n",
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      "Reward -  106.0\n",
      "Loss -  0.09274451434612274\n",
      "\n",
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      "Episode 792 :\n",
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      "Reward -  232.0\n",
      "Loss -  0.12172889709472656\n",
      "\n",
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      "Episode 793 :\n",
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      "Reward -  122.0\n",
      "Loss -  0.044893261045217514\n",
      "\n",
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      "Episode 794 :\n",
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      "Reward -  140.0\n",
      "Loss -  0.07436787337064743\n",
      "\n",
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      "Episode 795 :\n",
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      "Reward -  231.0\n",
      "Loss -  0.02891894057393074\n",
      "\n",
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      "Episode 796 :\n",
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      "Reward -  246.0\n",
      "Loss -  0.030721629038453102\n",
      "\n",
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      "Episode 797 :\n",
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      "Reward -  65.0\n",
      "Loss -  0.05124902352690697\n",
      "\n",
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      "Episode 798 :\n",
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      "Reward -  181.0\n",
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      "\n",
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      "Episode 799 :\n",
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      "Reward -  31.0\n",
      "Loss -  0.018434561789035797\n",
      "\n",
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      "Episode 800 :\n",
      "Step -  7255\n",
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      "Reward -  468.0\n",
      "Loss -  0.058513857424259186\n",
      "\n",
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      "Episode 801 :\n",
      "Step -  5526\n",
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      "Reward -  364.0\n",
      "Loss -  0.04806339740753174\n",
      "\n",
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      "Episode 802 :\n",
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      "Reward -  145.0\n",
      "Loss -  0.02485547587275505\n",
      "\n",
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      "Episode 803 :\n",
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      "Reward -  306.0\n",
      "Loss -  0.06976035237312317\n",
      "\n",
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      "Episode 804 :\n",
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      "Reward -  92.0\n",
      "Loss -  0.030495140701532364\n",
      "\n",
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      "Episode 805 :\n",
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      "Reward -  110.0\n",
      "Loss -  0.09918475896120071\n",
      "\n",
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      "Episode 806 :\n",
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      "Reward -  69.0\n",
      "Loss -  0.022000670433044434\n",
      "\n",
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      "Episode 807 :\n",
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      "Reward -  135.0\n",
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      "Episode 808 :\n",
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      "Reward -  217.0\n",
      "Loss -  0.037999436259269714\n",
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      "Episode 809 :\n",
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      "Reward -  76.0\n",
      "Loss -  0.05057871341705322\n",
      "\n",
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      "Episode 810 :\n",
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      "Reward -  93.0\n",
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      "Episode 811 :\n",
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      "Reward -  76.0\n",
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      "Episode 812 :\n",
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      "Reward -  96.0\n",
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      "Episode 813 :\n",
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      "Reward -  144.0\n",
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      "\n",
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      "Episode 814 :\n",
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      "Reward -  183.0\n",
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      "Episode 815 :\n",
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      "Reward -  143.0\n",
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      "Episode 816 :\n",
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      "Reward -  75.0\n",
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      "Episode 817 :\n",
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      "Episode 818 :\n",
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      "Reward -  330.0\n",
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      "Episode 819 :\n",
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      "\n",
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      "Episode 820 :\n",
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      "Reward -  83.0\n",
      "Loss -  0.07068164646625519\n",
      "\n",
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      "Episode 821 :\n",
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      "Reward -  36.0\n",
      "Loss -  0.08393823355436325\n",
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      "Episode 822 :\n",
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      "Reward -  241.0\n",
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      "Episode 823 :\n",
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      "Reward -  425.0\n",
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      "Episode 824 :\n",
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      "Reward -  30.0\n",
      "Loss -  0.03723776340484619\n",
      "\n",
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      "Episode 825 :\n",
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      "Reward -  93.0\n",
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      "Episode 826 :\n",
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      "Reward -  47.0\n",
      "Loss -  0.029365848749876022\n",
      "\n",
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      "Episode 827 :\n",
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      "Reward -  91.0\n",
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      "\n",
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      "Episode 828 :\n",
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      "Reward -  350.0\n",
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      "Episode 829 :\n",
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      "Reward -  574.0\n",
      "Loss -  0.029672354459762573\n",
      "\n",
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      "Episode 830 :\n",
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      "Reward -  90.0\n",
      "Loss -  0.04659264534711838\n",
      "\n",
      "\n",
      "Episode 831 :\n",
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      "\n",
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      "Episode 832 :\n",
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      "Reward -  142.0\n",
      "Loss -  0.041686736047267914\n",
      "\n",
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      "Episode 833 :\n",
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      "Reward -  90.0\n",
      "Loss -  0.05072340369224548\n",
      "\n",
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      "Episode 834 :\n",
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      "Reward -  90.0\n",
      "Loss -  0.13743121922016144\n",
      "\n",
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      "Episode 835 :\n",
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      "Reward -  128.0\n",
      "Loss -  0.07790494710206985\n",
      "\n",
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      "Episode 836 :\n",
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      "Reward -  78.0\n",
      "Loss -  0.08000785112380981\n",
      "\n",
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      "Episode 837 :\n",
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      "Reward -  93.0\n",
      "Loss -  0.05884992331266403\n",
      "\n",
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      "Episode 838 :\n",
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      "Reward -  28.0\n",
      "Loss -  0.04864739626646042\n",
      "\n",
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      "Episode 839 :\n",
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      "Reward -  49.0\n",
      "Loss -  0.03587494045495987\n",
      "\n",
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      "Episode 840 :\n",
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      "Reward -  45.0\n",
      "Loss -  0.03309105336666107\n",
      "\n",
      "\n",
      "Episode 841 :\n",
      "Step -  6124\n",
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      "Reward -  409.0\n",
      "Loss -  0.04228860139846802\n",
      "\n",
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      "Episode 842 :\n",
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      "Reward -  129.0\n",
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      "\n",
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      "Episode 843 :\n",
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      "Reward -  243.0\n",
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      "\n",
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      "Episode 844 :\n",
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      "Reward -  116.0\n",
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      "\n",
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      "Episode 845 :\n",
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      "\n",
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      "Episode 846 :\n",
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      "Reward -  75.0\n",
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      "\n",
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      "Episode 847 :\n",
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      "Reward -  153.0\n",
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      "\n",
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      "Episode 848 :\n",
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      "Reward -  265.0\n",
      "Loss -  0.06357432156801224\n",
      "\n",
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      "Episode 849 :\n",
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      "Reward -  17.0\n",
      "Loss -  0.2799120247364044\n",
      "\n",
      "\n",
      "Episode 850 :\n",
      "Step -  4507\n",
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      "Reward -  289.0\n",
      "Loss -  0.07867442071437836\n",
      "\n",
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      "Episode 851 :\n",
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      "Reward -  365.0\n",
      "Loss -  0.08301402628421783\n",
      "\n",
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      "Episode 852 :\n",
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      "Reward -  90.0\n",
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      "\n",
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      "Episode 853 :\n",
      "Step -  1422\n",
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      "Reward -  54.0\n",
      "Loss -  0.09408342093229294\n",
      "\n",
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      "Episode 854 :\n",
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      "Reward -  546.0\n",
      "Loss -  0.048929061740636826\n",
      "\n",
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      "Episode 855 :\n",
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      "Reward -  346.0\n",
      "Loss -  0.038308724761009216\n",
      "\n",
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      "Episode 856 :\n",
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      "Reward -  93.0\n",
      "Loss -  0.04508335143327713\n",
      "\n",
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      "Episode 857 :\n",
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      "Reward -  192.0\n",
      "Loss -  0.05823931097984314\n",
      "\n",
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      "Episode 858 :\n",
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      "Reward -  149.0\n",
      "Loss -  0.07960358262062073\n",
      "\n",
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      "Episode 859 :\n",
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      "Reward -  198.0\n",
      "Loss -  0.04530581831932068\n",
      "\n",
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      "Episode 860 :\n",
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      "Reward -  297.0\n",
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      "\n",
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      "Episode 861 :\n",
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      "Reward -  259.0\n",
      "Loss -  0.05840926989912987\n",
      "\n",
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      "Episode 862 :\n",
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      "Reward -  127.0\n",
      "Loss -  0.0631662979722023\n",
      "\n",
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      "Episode 863 :\n",
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      "Reward -  154.0\n",
      "Loss -  0.051427535712718964\n",
      "\n",
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      "Episode 864 :\n",
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      "Reward -  212.0\n",
      "Loss -  0.11433490365743637\n",
      "\n",
      "\n",
      "Episode 865 :\n",
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      "Reward -  412.0\n",
      "Loss -  0.05928822606801987\n",
      "\n",
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      "Episode 866 :\n",
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      "Reward -  348.0\n",
      "Loss -  0.11664031445980072\n",
      "\n",
      "\n",
      "Episode 867 :\n",
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      "Reward -  16.0\n",
      "Loss -  0.046373412013053894\n",
      "\n",
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      "Episode 868 :\n",
      "Step -  1934\n",
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      "Reward -  133.0\n",
      "Loss -  0.04233906790614128\n",
      "\n",
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      "Episode 869 :\n",
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      "Reward -  51.0\n",
      "Loss -  0.052245162427425385\n",
      "\n",
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      "Episode 870 :\n",
      "Step -  996\n",
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      "Reward -  51.0\n",
      "Loss -  0.04854528605937958\n",
      "\n",
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      "Episode 871 :\n",
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      "Reward -  114.0\n",
      "Loss -  0.05928465723991394\n",
      "\n",
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      "Episode 872 :\n",
      "Step -  997\n",
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      "Reward -  64.0\n",
      "Loss -  0.03034217655658722\n",
      "\n",
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      "Episode 873 :\n",
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      "Reward -  191.0\n",
      "Loss -  0.04435621201992035\n",
      "\n",
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      "Episode 874 :\n",
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      "Reward -  39.0\n",
      "Loss -  0.04682198166847229\n",
      "\n",
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      "Episode 875 :\n",
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      "Reward -  213.0\n",
      "Loss -  0.04630350321531296\n",
      "\n",
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      "Episode 876 :\n",
      "Step -  3138\n",
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      "Reward -  222.0\n",
      "Loss -  0.1070566177368164\n",
      "\n",
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      "Episode 877 :\n",
      "Step -  1230\n",
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      "Reward -  82.0\n",
      "Loss -  0.06477279216051102\n",
      "\n",
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      "Episode 878 :\n",
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      "Reward -  269.0\n",
      "Loss -  0.14296913146972656\n",
      "\n",
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      "Episode 879 :\n",
      "Step -  2695\n",
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      "Reward -  181.0\n",
      "Loss -  0.04012060910463333\n",
      "\n",
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      "Episode 880 :\n",
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      "Reward -  280.0\n",
      "Loss -  0.07516932487487793\n",
      "\n",
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      "Episode 881 :\n",
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      "Reward -  55.0\n",
      "Loss -  0.044411007314920425\n",
      "\n",
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      "Episode 882 :\n",
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      "Reward -  207.0\n",
      "Loss -  0.03926881030201912\n",
      "\n",
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      "Episode 883 :\n",
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      "Reward -  46.0\n",
      "Loss -  0.06115303188562393\n",
      "\n",
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      "Episode 884 :\n",
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      "Reward -  133.0\n",
      "Loss -  0.07753241062164307\n",
      "\n",
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      "Episode 885 :\n",
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      "Reward -  77.0\n",
      "Loss -  0.07834035158157349\n",
      "\n",
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      "Episode 886 :\n",
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      "Reward -  109.0\n",
      "Loss -  0.07048694789409637\n",
      "\n",
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      "Episode 887 :\n",
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      "Reward -  117.0\n",
      "Loss -  0.05898839980363846\n",
      "\n",
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      "Episode 888 :\n",
      "Step -  4342\n",
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      "Reward -  284.0\n",
      "Loss -  0.047202661633491516\n",
      "\n",
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      "Episode 889 :\n",
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      "Reward -  237.0\n",
      "Loss -  0.05383678153157234\n",
      "\n",
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      "Episode 890 :\n",
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      "Reward -  149.0\n",
      "Loss -  0.034003786742687225\n",
      "\n",
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      "Episode 891 :\n",
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      "Reward -  234.0\n",
      "Loss -  0.05299372225999832\n",
      "\n",
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      "Episode 892 :\n",
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      "Reward -  215.0\n",
      "Loss -  0.06814251840114594\n",
      "\n",
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      "Episode 893 :\n",
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      "Reward -  166.0\n",
      "Loss -  0.041126549243927\n",
      "\n",
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      "Episode 894 :\n",
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      "Reward -  145.0\n",
      "Loss -  0.07708937674760818\n",
      "\n",
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      "Episode 895 :\n",
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      "Reward -  57.0\n",
      "Loss -  0.04021882638335228\n",
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      "Episode 896 :\n",
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      "Reward -  241.0\n",
      "Loss -  0.040600407868623734\n",
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      "Episode 897 :\n",
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      "Reward -  120.0\n",
      "Loss -  0.06723429262638092\n",
      "\n",
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      "Episode 898 :\n",
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      "Reward -  141.0\n",
      "Loss -  0.05496635660529137\n",
      "\n",
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      "Episode 899 :\n",
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      "Reward -  114.0\n",
      "Loss -  0.04728727415204048\n",
      "\n",
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      "Episode 900 :\n",
      "Step -  3558\n",
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      "Reward -  234.0\n",
      "Loss -  0.04276201128959656\n",
      "\n",
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      "Episode 901 :\n",
      "Step -  1329\n",
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      "Reward -  85.0\n",
      "Loss -  0.05746549367904663\n",
      "\n",
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      "Episode 902 :\n",
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      "Reward -  59.0\n",
      "Loss -  0.03858437389135361\n",
      "\n",
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      "Episode 903 :\n",
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      "Reward -  117.0\n",
      "Loss -  0.09212414920330048\n",
      "\n",
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      "Episode 904 :\n",
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      "Reward -  130.0\n",
      "Loss -  0.05015386641025543\n",
      "\n",
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      "Episode 905 :\n",
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      "Reward -  76.0\n",
      "Loss -  0.08823144435882568\n",
      "\n",
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      "Episode 906 :\n",
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      "Reward -  47.0\n",
      "Loss -  0.07010077685117722\n",
      "\n",
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      "Episode 907 :\n",
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      "Reward -  205.0\n",
      "Loss -  0.037355199456214905\n",
      "\n",
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      "Episode 908 :\n",
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      "Reward -  194.0\n",
      "Loss -  0.03172500059008598\n",
      "\n",
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      "Episode 909 :\n",
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      "Reward -  174.0\n",
      "Loss -  0.06289346516132355\n",
      "\n",
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      "Episode 910 :\n",
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      "Reward -  250.0\n",
      "Loss -  0.04845988750457764\n",
      "\n",
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      "Episode 911 :\n",
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      "Reward -  219.0\n",
      "Loss -  0.0673389881849289\n",
      "\n",
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      "Episode 912 :\n",
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      "Reward -  188.0\n",
      "Loss -  0.05933789908885956\n",
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      "Episode 913 :\n",
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      "Reward -  168.0\n",
      "Loss -  0.049431268125772476\n",
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      "Episode 914 :\n",
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      "Reward -  167.0\n",
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      "Episode 915 :\n",
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      "Reward -  114.0\n",
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      "Episode 916 :\n",
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      "Reward -  123.0\n",
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      "Episode 917 :\n",
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      "Episode 918 :\n",
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      "Reward -  380.0\n",
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      "Episode 919 :\n",
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      "Reward -  257.0\n",
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      "Episode 920 :\n",
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      "Reward -  291.0\n",
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      "Episode 921 :\n",
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      "Reward -  226.0\n",
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      "\n",
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      "Episode 922 :\n",
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      "Reward -  157.0\n",
      "Loss -  0.04327329248189926\n",
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      "Episode 923 :\n",
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      "Reward -  85.0\n",
      "Loss -  0.058019399642944336\n",
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      "Episode 924 :\n",
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      "Reward -  165.0\n",
      "Loss -  0.07827520370483398\n",
      "\n",
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      "Episode 925 :\n",
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      "Reward -  266.0\n",
      "Loss -  0.05524613335728645\n",
      "\n",
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      "Episode 926 :\n",
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      "Reward -  134.0\n",
      "Loss -  0.06745575368404388\n",
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      "Episode 950 :\n",
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      "Episode 975 :\n",
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      "Episode 978 :\n",
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      "Reward -  130.0\n",
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      "Reward -  85.0\n",
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      "Reward -  178.0\n",
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      "\n",
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      "Episode 990 :\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Episode 995 :\n",
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      "Episode 1000 :\n",
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      "Episode 1001 :\n",
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      "Loss -  0.0898272693157196\n",
      "\n",
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      "Episode 1023 :\n",
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      "Reward -  315.0\n",
      "Loss -  0.027034170925617218\n",
      "\n",
      "\n",
      "Episode 1024 :\n",
      "Step -  2354\n",
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      "Reward -  164.0\n",
      "Loss -  0.04046756401658058\n",
      "\n",
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      "Episode 1025 :\n",
      "Step -  2007\n",
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      "Reward -  129.0\n",
      "Loss -  0.05948706716299057\n",
      "\n",
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      "Episode 1026 :\n",
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      "Reward -  139.0\n",
      "Loss -  0.038482654839754105\n",
      "\n",
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      "Episode 1027 :\n",
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      "Reward -  190.0\n",
      "Loss -  0.05228262022137642\n",
      "\n",
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      "Episode 1028 :\n",
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      "Reward -  328.0\n",
      "Loss -  0.047982171177864075\n",
      "\n",
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      "Episode 1029 :\n",
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      "Reward -  172.0\n",
      "Loss -  0.03926130011677742\n",
      "\n",
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      "Episode 1030 :\n",
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      "Reward -  145.0\n",
      "Loss -  0.11376407742500305\n",
      "\n",
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      "Episode 1031 :\n",
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      "Reward -  72.0\n",
      "Loss -  0.026702310889959335\n",
      "\n",
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      "Episode 1032 :\n",
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      "Reward -  207.0\n",
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      "\n",
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      "Episode 1033 :\n",
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      "Reward -  282.0\n",
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      "\n",
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      "Episode 1034 :\n",
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      "\n",
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      "Episode 1035 :\n",
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      "\n",
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      "Episode 1036 :\n",
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      "Reward -  114.0\n",
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      "\n",
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      "Episode 1037 :\n",
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      "Episode 1038 :\n",
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      "\n",
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      "Episode 1039 :\n",
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      "\n",
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      "Episode 1040 :\n",
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      "\n",
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      "Episode 1041 :\n",
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      "\n",
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      "Episode 1042 :\n",
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      "Reward -  88.0\n",
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      "Episode 1043 :\n",
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      "Reward -  71.0\n",
      "Loss -  0.11287586390972137\n",
      "\n",
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      "Episode 1044 :\n",
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      "Reward -  42.0\n",
      "Loss -  0.058032598346471786\n",
      "\n",
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      "Episode 1045 :\n",
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      "Reward -  88.0\n",
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      "\n",
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      "Episode 1046 :\n",
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      "Reward -  63.0\n",
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      "\n",
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      "Episode 1047 :\n",
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      "Reward -  99.0\n",
      "Loss -  0.9948307871818542\n",
      "\n",
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      "Episode 1048 :\n",
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      "Reward -  13.0\n",
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      "\n",
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      "Episode 1049 :\n",
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      "Reward -  278.0\n",
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      "\n",
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      "Episode 1050 :\n",
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      "Reward -  142.0\n",
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      "\n",
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      "Episode 1051 :\n",
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      "Reward -  178.0\n",
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      "\n",
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      "Episode 1052 :\n",
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      "Reward -  166.0\n",
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      "Episode 1053 :\n",
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      "Reward -  279.0\n",
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      "Episode 1054 :\n",
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      "Reward -  149.0\n",
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      "Episode 1055 :\n",
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      "Reward -  35.0\n",
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      "Episode 1056 :\n",
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      "Reward -  312.0\n",
      "Loss -  0.07799581438302994\n",
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      "Episode 1057 :\n",
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      "Reward -  230.0\n",
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      "\n",
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      "Episode 1058 :\n",
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      "Reward -  133.0\n",
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      "Episode 1059 :\n",
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      "Reward -  18.0\n",
      "Loss -  0.051762938499450684\n",
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      "Episode 1060 :\n",
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      "Reward -  35.0\n",
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      "Episode 1061 :\n",
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      "Reward -  240.0\n",
      "Loss -  0.04159603267908096\n",
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      "Episode 1062 :\n",
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      "Reward -  180.0\n",
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      "\n",
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      "Episode 1063 :\n",
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      "Reward -  84.0\n",
      "Loss -  0.04643888771533966\n",
      "\n",
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      "Episode 1064 :\n",
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      "Reward -  142.0\n",
      "Loss -  0.11278365552425385\n",
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      "Episode 1065 :\n",
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      "Reward -  517.0\n",
      "Loss -  0.12042474746704102\n",
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      "Episode 1066 :\n",
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      "Reward -  199.0\n",
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      "Episode 1067 :\n",
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      "Reward -  107.0\n",
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      "Episode 1068 :\n",
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      "Reward -  293.0\n",
      "Loss -  0.029937293380498886\n",
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      "Episode 1069 :\n",
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      "Reward -  107.0\n",
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      "Episode 1070 :\n",
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      "Reward -  54.0\n",
      "Loss -  0.0826437920331955\n",
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      "Episode 1071 :\n",
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      "Reward -  112.0\n",
      "Loss -  0.043520379811525345\n",
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      "Episode 1072 :\n",
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      "Reward -  281.0\n",
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      "Episode 1073 :\n",
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      "Episode 1074 :\n",
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      "Reward -  76.0\n",
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      "Reward -  136.0\n",
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      "Episode 1076 :\n",
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      "Episode 1077 :\n",
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      "Reward -  172.0\n",
      "Loss -  0.09166905283927917\n",
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      "Episode 1078 :\n",
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      "Episode 1079 :\n",
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      "Reward -  123.0\n",
      "Loss -  0.01921367645263672\n",
      "\n",
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      "Episode 1080 :\n",
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      "Reward -  140.0\n",
      "Loss -  0.07560013234615326\n",
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      "Episode 1081 :\n",
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      "Reward -  26.0\n",
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      "Episode 1082 :\n",
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      "Reward -  140.0\n",
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      "Reward -  18.0\n",
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      "Episode 1084 :\n",
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      "Reward -  414.0\n",
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      "Reward -  353.0\n",
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      "Episode 1086 :\n",
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      "Reward -  182.0\n",
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      "Reward -  23.0\n",
      "Loss -  0.06908400356769562\n",
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      "Reward -  379.0\n",
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      "Reward -  303.0\n",
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      "Episode 1090 :\n",
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      "Reward -  81.0\n",
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      "Episode 1091 :\n",
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      "Reward -  219.0\n",
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      "Reward -  236.0\n",
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      "Episode 1100 :\n",
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      "Reward -  70.0\n",
      "Loss -  0.031345903873443604\n",
      "\n",
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      "Episode 1101 :\n",
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      "Episode 1111 :\n",
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      "\n",
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      "Episode 1119 :\n",
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      "\n",
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      "Episode 1120 :\n",
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      "Reward -  66.0\n",
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      "\n",
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      "Episode 1121 :\n",
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      "\n",
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      "\n",
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      "\n",
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      "Episode 1125 :\n",
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      "\n",
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      "\n",
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      "Episode 1127 :\n",
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      "Reward -  118.0\n",
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      "\n",
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      "Episode 1128 :\n",
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      "Reward -  135.0\n",
      "Loss -  0.045715365558862686\n",
      "\n",
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      "Episode 1129 :\n",
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      "Reward -  259.0\n",
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      "\n",
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      "Episode 1130 :\n",
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      "Reward -  92.0\n",
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      "\n",
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      "Episode 1131 :\n",
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      "Reward -  168.0\n",
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      "\n",
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      "\n",
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      "Episode 1133 :\n",
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      "\n",
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      "Episode 1134 :\n",
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      "\n",
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      "Episode 1135 :\n",
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      "\n",
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      "Episode 1136 :\n",
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      "Reward -  270.0\n",
      "Loss -  0.08901798725128174\n",
      "\n",
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      "Episode 1137 :\n",
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      "\n",
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      "Episode 1138 :\n",
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      "Reward -  138.0\n",
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      "\n",
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      "Episode 1139 :\n",
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      "Reward -  303.0\n",
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      "\n",
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      "Episode 1140 :\n",
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      "Loss -  0.08263329416513443\n",
      "\n",
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      "Episode 1141 :\n",
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      "Reward -  466.0\n",
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      "\n",
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      "Episode 1142 :\n",
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      "Reward -  270.0\n",
      "Loss -  0.04425227642059326\n",
      "\n",
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      "Episode 1143 :\n",
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      "Reward -  100.0\n",
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      "\n",
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      "Episode 1144 :\n",
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      "Reward -  208.0\n",
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      "\n",
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      "Episode 1145 :\n",
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      "\n",
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      "Episode 1146 :\n",
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      "Reward -  46.0\n",
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      "\n",
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      "Episode 1147 :\n",
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      "Reward -  125.0\n",
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      "\n",
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      "Episode 1148 :\n",
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      "\n",
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      "Episode 1149 :\n",
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      "Reward -  85.0\n",
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      "\n",
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      "Episode 1150 :\n",
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      "Reward -  185.0\n",
      "Loss -  0.10683289170265198\n",
      "\n",
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      "Episode 1151 :\n",
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      "Reward -  24.0\n",
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      "\n",
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      "Episode 1152 :\n",
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      "Reward -  84.0\n",
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      "\n",
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      "Episode 1153 :\n",
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      "Reward -  141.0\n",
      "Loss -  0.0656033456325531\n",
      "\n",
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      "Episode 1154 :\n",
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      "Reward -  152.0\n",
      "Loss -  0.07559524476528168\n",
      "\n",
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      "Episode 1155 :\n",
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      "Reward -  120.0\n",
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      "\n",
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      "Episode 1156 :\n",
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      "Reward -  323.0\n",
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      "\n",
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      "Episode 1157 :\n",
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      "Reward -  197.0\n",
      "Loss -  0.019176501780748367\n",
      "\n",
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      "Episode 1158 :\n",
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      "Reward -  165.0\n",
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      "\n",
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      "Episode 1159 :\n",
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      "Reward -  182.0\n",
      "Loss -  0.04344629496335983\n",
      "\n",
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      "Episode 1160 :\n",
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      "Reward -  28.0\n",
      "Loss -  0.04359344393014908\n",
      "\n",
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      "Episode 1161 :\n",
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      "Reward -  85.0\n",
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      "\n",
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      "Reward -  106.0\n",
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      "\n",
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      "Episode 1163 :\n",
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      "Reward -  250.0\n",
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      "\n",
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      "Episode 1164 :\n",
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      "Reward -  80.0\n",
      "Loss -  0.06909751892089844\n",
      "\n",
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      "Reward -  139.0\n",
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      "\n",
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      "Reward -  210.0\n",
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      "\n",
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      "Reward -  292.0\n",
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      "\n",
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      "Episode 1168 :\n",
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      "Reward -  255.0\n",
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      "\n",
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      "Episode 1169 :\n",
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      "Reward -  138.0\n",
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      "\n",
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      "Episode 1170 :\n",
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      "Reward -  95.0\n",
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      "\n",
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      "Episode 1171 :\n",
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      "Reward -  101.0\n",
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      "\n",
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      "\n",
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      "Reward -  183.0\n",
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      "\n",
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      "Reward -  263.0\n",
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      "\n",
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      "Reward -  193.0\n",
      "Loss -  0.16862261295318604\n",
      "\n",
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      "Episode 1177 :\n",
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      "Reward -  69.0\n",
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      "\n",
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      "\n",
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      "Reward -  49.0\n",
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      "\n",
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      "Reward -  93.0\n",
      "Loss -  0.060063235461711884\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Episode 1191 :\n",
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      "Reward -  392.0\n",
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      "Reward -  418.0\n",
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      "\n",
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      "Reward -  216.0\n",
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      "Reward -  123.0\n",
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      "Reward -  44.0\n",
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      "Reward -  233.0\n",
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      "\n",
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      "Episode 1200 :\n",
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      "\n",
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      "Episode 1251 :\n",
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     ]
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     "output_type": "stream",
     "text": [
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      "\n",
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      "Episode 1311 :\n",
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      "Episode 1313 :\n",
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      "Episode 1321 :\n",
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      "Episode 1330 :\n",
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      "\n",
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      "Episode 1333 :\n",
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      "\n",
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      "Episode 1334 :\n",
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      "Reward -  78.0\n",
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      "Episode 1335 :\n",
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      "Episode 1336 :\n",
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      "Episode 1337 :\n",
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      "Episode 1338 :\n",
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      "\n",
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      "Episode 1341 :\n",
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      "\n",
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      "\n",
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      "Episode 1346 :\n",
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      "Episode 1347 :\n",
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      "Episode 1349 :\n",
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      "Episode 1350 :\n",
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      "Episode 1351 :\n",
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      "Episode 1354 :\n",
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      "Episode 1360 :\n",
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      "Episode 1361 :\n",
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      "Reward -  184.0\n",
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      "\n",
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      "Episode 1406 :\n",
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      "Reward -  169.0\n",
      "Loss -  0.04928407818078995\n",
      "\n",
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      "Episode 1407 :\n",
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      "Reward -  176.0\n",
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      "\n",
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      "Episode 1408 :\n",
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      "Reward -  134.0\n",
      "Loss -  0.03544243425130844\n",
      "\n",
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      "Episode 1409 :\n",
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      "Reward -  282.0\n",
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      "\n",
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      "Episode 1410 :\n",
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      "\n",
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      "Episode 1411 :\n",
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      "Reward -  147.0\n",
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      "\n",
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      "Episode 1412 :\n",
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      "Reward -  282.0\n",
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      "Episode 1413 :\n",
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      "Episode 1414 :\n",
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      "Episode 1415 :\n",
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      "Episode 1416 :\n",
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      "Episode 1417 :\n",
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      "Episode 1418 :\n",
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      "Episode 1419 :\n",
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      "Episode 1420 :\n",
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      "Episode 1421 :\n",
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      "Episode 1422 :\n",
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      "Episode 1423 :\n",
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      "Episode 1424 :\n",
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      "Episode 1425 :\n",
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      "Episode 1426 :\n",
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      "Episode 1427 :\n",
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      "Episode 1428 :\n",
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      "Episode 1429 :\n",
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      "Episode 1430 :\n",
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      "Episode 1431 :\n",
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      "Episode 1432 :\n",
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      "Episode 1433 :\n",
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      "Episode 1434 :\n",
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      "Episode 1435 :\n",
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      "Episode 1436 :\n",
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      "Episode 1437 :\n",
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      "Episode 1438 :\n",
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      "Episode 1439 :\n",
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      "Episode 1440 :\n",
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      "Episode 1441 :\n",
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      "Episode 1442 :\n",
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      "Episode 1443 :\n",
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      "Episode 1444 :\n",
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      "Episode 1445 :\n",
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      "Episode 1447 :\n",
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      "Episode 1449 :\n",
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      "Episode 1450 :\n",
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      "Episode 1451 :\n",
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      "Episode 1452 :\n",
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      "Episode 1453 :\n",
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      "Episode 1454 :\n",
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      "Episode 1455 :\n",
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      "Episode 1456 :\n",
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      "Episode 1458 :\n",
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      "Episode 1500 :\n",
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      "Episode 1501 :\n",
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      "\n",
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    {
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     "output_type": "stream",
     "text": [
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      "Loss -  0.043109677731990814\n",
      "\n",
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      "Episode 1598 :\n",
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      "Reward -  220.0\n",
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      "\n",
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      "Episode 1599 :\n",
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      "Reward -  180.0\n",
      "Loss -  0.04440223425626755\n",
      "\n",
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      "Episode 1600 :\n",
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      "Reward -  323.0\n",
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      "\n",
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      "Episode 1601 :\n",
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      "\n",
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      "Episode 1602 :\n",
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      "\n",
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      "Episode 1603 :\n",
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      "Episode 1604 :\n",
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      "Episode 1605 :\n",
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      "\n",
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      "Episode 1606 :\n",
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      "Reward -  134.0\n",
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      "\n",
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      "Episode 1607 :\n",
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      "\n",
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      "Episode 1608 :\n",
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      "\n",
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      "Episode 1609 :\n",
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      "Episode 1610 :\n",
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      "Episode 1611 :\n",
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      "Episode 1612 :\n",
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      "Episode 1614 :\n",
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      "Episode 1615 :\n",
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      "Episode 1616 :\n",
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      "Episode 1617 :\n",
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      "Episode 1618 :\n",
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      "Reward -  225.0\n",
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      "Episode 1619 :\n",
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      "Episode 1620 :\n",
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      "Episode 1621 :\n",
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      "Episode 1622 :\n",
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      "Episode 1623 :\n",
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      "Episode 1624 :\n",
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      "Episode 1625 :\n",
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      "Reward -  67.0\n",
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      "Episode 1626 :\n",
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      "Episode 1627 :\n",
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      "Episode 1628 :\n",
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      "Episode 1629 :\n",
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      "Episode 1630 :\n",
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      "Episode 1631 :\n",
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      "Episode 1632 :\n",
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      "Episode 1633 :\n",
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      "Episode 1634 :\n",
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      "Episode 1635 :\n",
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      "Episode 1636 :\n",
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      "Episode 1637 :\n",
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      "Episode 1638 :\n",
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      "Episode 1639 :\n",
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      "Episode 1640 :\n",
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      "\n",
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      "Episode 1641 :\n",
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      "\n",
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      "Episode 1642 :\n",
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      "\n",
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      "Episode 1643 :\n",
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      "Episode 1647 :\n",
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      "Episode 1648 :\n",
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      "Episode 1649 :\n",
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      "Episode 1650 :\n",
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      "Episode 1651 :\n",
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      "Episode 1653 :\n",
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      "Episode 1654 :\n",
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      "Episode 1656 :\n",
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      "Episode 1657 :\n",
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      "Episode 1659 :\n",
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      "Episode 1660 :\n",
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      "Episode 1693 :\n",
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      "Reward -  261.0\n",
      "Loss -  0.03799332678318024\n",
      "\n",
      "\n",
      "Episode 1694 :\n",
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      "Reward -  47.0\n",
      "Loss -  0.11550736427307129\n",
      "\n",
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      "Episode 1695 :\n",
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      "Reward -  265.0\n",
      "Loss -  0.03876146674156189\n",
      "\n",
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      "Episode 1696 :\n",
      "Step -  3840\n",
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      "Reward -  261.0\n",
      "Loss -  0.04957728087902069\n",
      "\n",
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      "Episode 1697 :\n",
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      "Reward -  43.0\n",
      "Loss -  0.13399693369865417\n",
      "\n",
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      "Episode 1698 :\n",
      "Step -  7313\n",
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      "Reward -  503.0\n",
      "Loss -  0.06981244683265686\n",
      "\n",
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      "Episode 1699 :\n",
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      "Reward -  111.0\n",
      "Loss -  0.09122276306152344\n",
      "\n",
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      "Episode 1700 :\n",
      "Step -  3531\n",
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      "Reward -  227.0\n",
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      "\n",
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      "Episode 1701 :\n",
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      "\n",
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      "Episode 1702 :\n",
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      "Reward -  616.0\n",
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      "\n",
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      "Episode 1703 :\n",
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      "\n",
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      "Episode 1704 :\n",
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      "\n",
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      "Episode 1705 :\n",
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      "Reward -  272.0\n",
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      "\n",
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      "Episode 1706 :\n",
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      "Reward -  266.0\n",
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      "\n",
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      "Episode 1707 :\n",
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      "Episode 1708 :\n",
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      "Episode 1709 :\n",
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      "\n",
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      "Episode 1710 :\n",
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      "Episode 1711 :\n",
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      "Episode 1712 :\n",
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      "\n",
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      "Episode 1713 :\n",
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      "Reward -  213.0\n",
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      "\n",
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      "Episode 1714 :\n",
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      "Episode 1715 :\n",
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      "\n",
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      "Episode 1716 :\n",
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      "\n",
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      "Episode 1717 :\n",
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      "\n",
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      "Episode 1718 :\n",
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      "Episode 1719 :\n",
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      "\n",
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      "Episode 1720 :\n",
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      "Episode 1721 :\n",
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      "Episode 1722 :\n",
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      "Episode 1723 :\n",
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      "Episode 1724 :\n",
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      "Episode 1725 :\n",
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      "Episode 1726 :\n",
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      "\n",
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      "Episode 1727 :\n",
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      "Episode 1728 :\n",
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      "\n",
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      "Episode 1729 :\n",
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      "Episode 1730 :\n",
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      "Episode 1731 :\n",
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      "Reward -  135.0\n",
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      "Episode 1732 :\n",
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      "Reward -  68.0\n",
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      "Episode 1733 :\n",
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      "Reward -  144.0\n",
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      "\n",
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      "Episode 1734 :\n",
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      "Episode 1735 :\n",
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      "Episode 1736 :\n",
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      "Episode 1737 :\n",
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      "Episode 1738 :\n",
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      "Episode 1739 :\n",
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      "Episode 1740 :\n",
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      "Episode 1741 :\n",
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      "Episode 1742 :\n",
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      "Reward -  621.0\n",
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      "Episode 1743 :\n",
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      "Episode 1744 :\n",
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      "Episode 1745 :\n",
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      "Episode 1746 :\n",
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      "Episode 1747 :\n",
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      "Episode 1748 :\n",
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      "Episode 1750 :\n",
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      "Episode 1751 :\n",
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      "Episode 1752 :\n",
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      "Episode 1753 :\n",
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      "Episode 1754 :\n",
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      "Episode 1755 :\n",
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      "Episode 1756 :\n",
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      "Episode 1760 :\n",
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      "Episode 1761 :\n",
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      "Episode 1762 :\n",
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      "Episode 1763 :\n",
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      "Episode 1764 :\n",
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      "Episode 1765 :\n",
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      "Episode 1767 :\n",
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      "Episode 1769 :\n",
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      "Reward -  130.0\n",
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      "Episode 1789 :\n",
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      "Reward -  148.0\n",
      "Loss -  0.048889413475990295\n",
      "\n",
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      "Episode 1790 :\n",
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      "Reward -  174.0\n",
      "Loss -  0.04315900057554245\n",
      "\n",
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      "Episode 1791 :\n",
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      "Reward -  339.0\n",
      "Loss -  0.05805591866374016\n",
      "\n",
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      "Episode 1792 :\n",
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      "Reward -  41.0\n",
      "Loss -  0.06304159015417099\n",
      "\n",
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      "Episode 1793 :\n",
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      "Reward -  402.0\n",
      "Loss -  0.07648954540491104\n",
      "\n",
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      "Episode 1794 :\n",
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      "Reward -  126.0\n",
      "Loss -  0.04744891822338104\n",
      "\n",
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      "Episode 1795 :\n",
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      "Reward -  268.0\n",
      "Loss -  0.10690391063690186\n",
      "\n",
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      "Episode 1796 :\n",
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      "Reward -  65.0\n",
      "Loss -  0.17108416557312012\n",
      "\n",
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      "Episode 1797 :\n",
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      "Reward -  210.0\n",
      "Loss -  0.05171561986207962\n",
      "\n",
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      "Episode 1798 :\n",
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      "Reward -  203.0\n",
      "Loss -  0.1807442605495453\n",
      "\n",
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      "Episode 1799 :\n",
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      "Reward -  444.0\n",
      "Loss -  0.034756749868392944\n",
      "\n",
      "\n",
      "Episode 1800 :\n",
      "Step -  2720\n",
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      "Reward -  185.0\n",
      "Loss -  0.10514485836029053\n",
      "\n",
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      "Episode 1801 :\n",
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      "Reward -  233.0\n",
      "Loss -  0.044368404895067215\n",
      "\n",
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      "Episode 1802 :\n",
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      "Reward -  489.0\n",
      "Loss -  0.06109067425131798\n",
      "\n",
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      "Episode 1803 :\n",
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      "Reward -  541.0\n",
      "Loss -  0.10905829071998596\n",
      "\n",
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      "Episode 1804 :\n",
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      "Reward -  898.0\n",
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      "\n",
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      "Episode 1805 :\n",
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      "Reward -  77.0\n",
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      "\n",
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      "Episode 1806 :\n",
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      "Reward -  111.0\n",
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      "\n",
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      "Episode 1807 :\n",
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      "Reward -  250.0\n",
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      "\n",
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      "Episode 1808 :\n",
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      "Reward -  463.0\n",
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      "\n",
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      "Episode 1809 :\n",
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      "Reward -  140.0\n",
      "Loss -  0.03580252081155777\n",
      "\n",
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      "Episode 1810 :\n",
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      "Reward -  353.0\n",
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      "\n",
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      "Episode 1811 :\n",
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      "Reward -  58.0\n",
      "Loss -  0.061266008764505386\n",
      "\n",
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      "Episode 1812 :\n",
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      "Reward -  341.0\n",
      "Loss -  0.06437525153160095\n",
      "\n",
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      "Episode 1813 :\n",
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      "Reward -  212.0\n",
      "Loss -  0.0925433561205864\n",
      "\n",
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      "Episode 1814 :\n",
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      "Reward -  230.0\n",
      "Loss -  0.03009111061692238\n",
      "\n",
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      "Episode 1815 :\n",
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      "Reward -  346.0\n",
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      "\n",
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      "Episode 1816 :\n",
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      "Reward -  511.0\n",
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      "\n",
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      "Episode 1817 :\n",
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      "Reward -  404.0\n",
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      "\n",
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      "Episode 1818 :\n",
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      "Reward -  137.0\n",
      "Loss -  0.06741291284561157\n",
      "\n",
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      "Episode 1819 :\n",
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      "Reward -  153.0\n",
      "Loss -  0.059783048927783966\n",
      "\n",
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      "Episode 1820 :\n",
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      "Reward -  47.0\n",
      "Loss -  0.07489633560180664\n",
      "\n",
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      "Episode 1821 :\n",
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      "Reward -  219.0\n",
      "Loss -  0.0574539378285408\n",
      "\n",
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      "Episode 1822 :\n",
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      "Reward -  795.0\n",
      "Loss -  0.042057305574417114\n",
      "\n",
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      "Episode 1823 :\n",
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      "Reward -  138.0\n",
      "Loss -  0.037215910851955414\n",
      "\n",
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      "Episode 1824 :\n",
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      "Reward -  298.0\n",
      "Loss -  0.07278980314731598\n",
      "\n",
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      "Episode 1825 :\n",
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      "Reward -  145.0\n",
      "Loss -  0.057959821075201035\n",
      "\n",
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      "Episode 1826 :\n",
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      "Reward -  43.0\n",
      "Loss -  0.04786558449268341\n",
      "\n",
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      "Episode 1827 :\n",
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      "Reward -  182.0\n",
      "Loss -  0.06318968534469604\n",
      "\n",
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      "Episode 1828 :\n",
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      "Reward -  201.0\n",
      "Loss -  0.0700228363275528\n",
      "\n",
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      "Episode 1829 :\n",
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      "Reward -  169.0\n",
      "Loss -  0.03907908499240875\n",
      "\n",
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      "Episode 1830 :\n",
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      "Reward -  154.0\n",
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      "\n",
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      "Episode 1831 :\n",
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      "Reward -  161.0\n",
      "Loss -  0.11650334298610687\n",
      "\n",
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      "Episode 1832 :\n",
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      "Reward -  411.0\n",
      "Loss -  0.11030784994363785\n",
      "\n",
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      "Episode 1833 :\n",
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      "Reward -  186.0\n",
      "Loss -  0.031079618260264397\n",
      "\n",
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      "Episode 1834 :\n",
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      "Reward -  159.0\n",
      "Loss -  0.07169105112552643\n",
      "\n",
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      "Episode 1835 :\n",
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      "Reward -  123.0\n",
      "Loss -  0.04527246206998825\n",
      "\n",
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      "Episode 1836 :\n",
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      "Reward -  74.0\n",
      "Loss -  0.07060876488685608\n",
      "\n",
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      "Episode 1837 :\n",
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      "Reward -  269.0\n",
      "Loss -  0.038415610790252686\n",
      "\n",
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      "Episode 1838 :\n",
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      "Reward -  80.0\n",
      "Loss -  0.11701662093400955\n",
      "\n",
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      "Episode 1839 :\n",
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      "Reward -  295.0\n",
      "Loss -  0.08236885815858841\n",
      "\n",
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      "Episode 1840 :\n",
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      "Reward -  285.0\n",
      "Loss -  0.04689822345972061\n",
      "\n",
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      "Episode 1841 :\n",
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      "Reward -  151.0\n",
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      "Episode 1842 :\n",
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      "\n",
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      "Episode 1843 :\n",
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      "Reward -  187.0\n",
      "Loss -  0.08005495369434357\n",
      "\n",
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      "Episode 1844 :\n",
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      "Reward -  122.0\n",
      "Loss -  0.059163760393857956\n",
      "\n",
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      "Reward -  233.0\n",
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      "\n",
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      "Reward -  192.0\n",
      "Loss -  0.06779910624027252\n",
      "\n",
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      "Episode 1847 :\n",
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      "Reward -  119.0\n",
      "Loss -  0.04169600084424019\n",
      "\n",
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      "Episode 1848 :\n",
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      "Reward -  125.0\n",
      "Loss -  0.06872782856225967\n",
      "\n",
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      "Episode 1849 :\n",
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      "Reward -  397.0\n",
      "Loss -  0.03971036896109581\n",
      "\n",
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      "Episode 1850 :\n",
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      "Reward -  222.0\n",
      "Loss -  0.09317205846309662\n",
      "\n",
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      "Episode 1851 :\n",
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      "Reward -  361.0\n",
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      "Episode 1852 :\n",
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      "Episode 1853 :\n",
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      "Reward -  312.0\n",
      "Loss -  0.059420160949230194\n",
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      "Episode 1854 :\n",
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      "Reward -  73.0\n",
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      "Episode 1856 :\n",
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      "Reward -  148.0\n",
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      "Episode 1857 :\n",
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      "Reward -  160.0\n",
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      "\n",
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      "Episode 1860 :\n",
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      "Reward -  308.0\n",
      "Loss -  0.08640141785144806\n",
      "\n",
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      "Episode 1861 :\n",
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      "Reward -  72.0\n",
      "Loss -  0.7524821162223816\n",
      "\n",
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      "Episode 1862 :\n",
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      "Reward -  291.0\n",
      "Loss -  0.05718515068292618\n",
      "\n",
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      "Episode 1863 :\n",
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      "Reward -  184.0\n",
      "Loss -  0.03027527593076229\n",
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      "Episode 1864 :\n",
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      "Reward -  365.0\n",
      "Loss -  0.10424153506755829\n",
      "\n",
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      "Episode 1865 :\n",
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      "Reward -  214.0\n",
      "Loss -  0.04280933365225792\n",
      "\n",
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      "Episode 1866 :\n",
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      "Reward -  346.0\n",
      "Loss -  0.07295344769954681\n",
      "\n",
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      "Episode 1867 :\n",
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      "Reward -  178.0\n",
      "Loss -  0.09732864052057266\n",
      "\n",
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      "Episode 1868 :\n",
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      "Reward -  223.0\n",
      "Loss -  0.11680851876735687\n",
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      "Episode 1869 :\n",
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      "Reward -  1189.0\n",
      "Loss -  0.04476258531212807\n",
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      "Episode 1870 :\n",
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      "Reward -  219.0\n",
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      "Episode 1872 :\n",
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      "Reward -  286.0\n",
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      "Reward -  29.0\n",
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      "Reward -  25.0\n",
      "Loss -  0.0032481022644788027\n",
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      "Episode 1876 :\n",
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      "Reward -  8.0\n",
      "Loss -  0.0015943266917020082\n",
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      "Reward -  26.0\n",
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      "Episode 1878 :\n",
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      "Reward -  96.0\n",
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      "Episode 1880 :\n",
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      "Reward -  99.0\n",
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      "\n",
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      "Episode 1882 :\n",
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      "Reward -  35.0\n",
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      "\n",
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      "Episode 1884 :\n",
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      "Reward -  68.0\n",
      "Loss -  1.4986202716827393\n",
      "\n",
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      "Episode 1885 :\n",
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      "Cumulative steps -  5454745\n",
      "Reward -  5.0\n",
      "Loss -  0.04700092971324921\n",
      "\n",
      "\n",
      "Episode 1886 :\n",
      "Step -  261\n",
      "Cumulative steps -  5455006\n",
      "Reward -  1.0\n",
      "Loss -  1.525515079498291\n",
      "\n",
      "\n",
      "Episode 1887 :\n",
      "Step -  966\n",
      "Cumulative steps -  5455972\n",
      "Reward -  8.0\n",
      "Loss -  0.009616020135581493\n",
      "\n",
      "\n",
      "Episode 1888 :\n",
      "Step -  1542\n",
      "Cumulative steps -  5457514\n",
      "Reward -  68.0\n",
      "Loss -  0.022335611283779144\n",
      "\n",
      "\n",
      "Episode 1889 :\n",
      "Step -  987\n",
      "Cumulative steps -  5458501\n",
      "Reward -  64.0\n",
      "Loss -  0.03562730550765991\n",
      "\n",
      "\n",
      "Episode 1890 :\n",
      "Step -  1116\n",
      "Cumulative steps -  5459617\n",
      "Reward -  25.0\n",
      "Loss -  0.009708510711789131\n",
      "\n",
      "\n",
      "Episode 1891 :\n",
      "Step -  1498\n",
      "Cumulative steps -  5461115\n",
      "Reward -  30.0\n",
      "Loss -  0.016305427998304367\n",
      "\n",
      "\n",
      "Episode 1892 :\n",
      "Step -  2670\n",
      "Cumulative steps -  5463785\n",
      "Reward -  90.0\n",
      "Loss -  0.028715848922729492\n",
      "\n",
      "\n",
      "Episode 1893 :\n",
      "Step -  850\n",
      "Cumulative steps -  5464635\n",
      "Reward -  26.0\n",
      "Loss -  0.04385844245553017\n",
      "\n",
      "\n",
      "Episode 1894 :\n",
      "Step -  1977\n",
      "Cumulative steps -  5466612\n",
      "Reward -  80.0\n",
      "Loss -  0.01786724105477333\n",
      "\n",
      "\n",
      "Episode 1895 :\n",
      "Step -  914\n",
      "Cumulative steps -  5467526\n",
      "Reward -  39.0\n",
      "Loss -  0.014875510707497597\n",
      "\n",
      "\n",
      "Episode 1896 :\n",
      "Step -  1418\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative steps -  5468944\n",
      "Reward -  36.0\n",
      "Loss -  0.014847343787550926\n",
      "\n",
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      "Episode 1897 :\n",
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      "Loss -  1.7241034507751465\n",
      "\n",
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      "Episode 1898 :\n",
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      "\n",
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      "Episode 1899 :\n",
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      "Loss -  0.019276795908808708\n",
      "\n",
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      "Episode 1900 :\n",
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      "Reward -  62.0\n",
      "Loss -  0.012599140405654907\n",
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      "\n",
      "Episode 1901 :\n",
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      "Reward -  41.0\n",
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      "\n",
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      "Episode 1902 :\n",
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      "Reward -  70.0\n",
      "Loss -  0.02385641634464264\n",
      "\n",
      "\n",
      "Episode 1903 :\n",
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      "Reward -  25.0\n",
      "Loss -  0.055655792355537415\n",
      "\n",
      "\n",
      "Episode 1904 :\n",
      "Step -  589\n",
      "Cumulative steps -  5264\n",
      "Reward -  16.0\n",
      "Loss -  0.028587158769369125\n",
      "\n",
      "\n",
      "Episode 1905 :\n",
      "Step -  467\n",
      "Cumulative steps -  5732\n",
      "Reward -  8.0\n",
      "Loss -  0.022008221596479416\n",
      "\n",
      "\n",
      "Episode 1906 :\n",
      "Step -  1669\n",
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      "Reward -  88.0\n",
      "Loss -  0.025450220331549644\n",
      "\n",
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      "Episode 1907 :\n",
      "Step -  1100\n",
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      "Reward -  34.0\n",
      "Loss -  0.025546323508024216\n",
      "\n",
      "\n",
      "Episode 1908 :\n",
      "Step -  1477\n",
      "Cumulative steps -  9981\n",
      "Reward -  60.0\n",
      "Loss -  0.00897967629134655\n",
      "\n",
      "\n",
      "Episode 1909 :\n",
      "Step -  748\n",
      "Cumulative steps -  10730\n",
      "Reward -  3.0\n",
      "Loss -  0.06267902255058289\n",
      "\n",
      "\n",
      "Episode 1910 :\n",
      "Step -  1631\n",
      "Cumulative steps -  12362\n",
      "Reward -  40.0\n",
      "Loss -  0.02523641474545002\n",
      "\n",
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      "Episode 1911 :\n",
      "Step -  1040\n",
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      "Reward -  70.0\n",
      "Loss -  0.044584009796381\n",
      "\n",
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      "Episode 1912 :\n",
      "Step -  2443\n",
      "Cumulative steps -  15847\n",
      "Reward -  133.0\n",
      "Loss -  0.01808343641459942\n",
      "\n",
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      "Episode 1913 :\n",
      "Step -  3597\n",
      "Cumulative steps -  19445\n",
      "Reward -  192.0\n",
      "Loss -  0.04852056875824928\n",
      "\n",
      "\n",
      "Episode 1914 :\n",
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      "Cumulative steps -  20484\n",
      "Reward -  14.0\n",
      "Loss -  0.02352732978761196\n",
      "\n",
      "\n",
      "Episode 1915 :\n",
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      "Reward -  98.0\n",
      "Loss -  0.03505683317780495\n",
      "\n",
      "\n",
      "Episode 1916 :\n",
      "Step -  1090\n",
      "Cumulative steps -  23384\n",
      "Reward -  36.0\n",
      "Loss -  0.025454025715589523\n",
      "\n",
      "\n",
      "Episode 1917 :\n",
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      "Reward -  114.0\n",
      "Loss -  0.013875504955649376\n",
      "\n",
      "\n",
      "Episode 1918 :\n",
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      "Cumulative steps -  27375\n",
      "Reward -  77.0\n",
      "Loss -  0.018845953047275543\n",
      "\n",
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      "Episode 1919 :\n",
      "Step -  1793\n",
      "Cumulative steps -  29169\n",
      "Reward -  54.0\n",
      "Loss -  0.027793070301413536\n",
      "\n",
      "\n",
      "Episode 1920 :\n",
      "Step -  2614\n",
      "Cumulative steps -  31784\n",
      "Reward -  105.0\n",
      "Loss -  0.026899240911006927\n",
      "\n",
      "\n",
      "Episode 1921 :\n",
      "Step -  1558\n",
      "Cumulative steps -  33343\n",
      "Reward -  29.0\n",
      "Loss -  0.018859850242733955\n",
      "\n",
      "\n",
      "Episode 1922 :\n",
      "Step -  1426\n",
      "Cumulative steps -  34770\n",
      "Reward -  79.0\n",
      "Loss -  0.031046001240611076\n",
      "\n",
      "\n",
      "Episode 1923 :\n",
      "Step -  4099\n",
      "Cumulative steps -  38870\n",
      "Reward -  248.0\n",
      "Loss -  0.018941111862659454\n",
      "\n",
      "\n",
      "Episode 1924 :\n",
      "Step -  1947\n",
      "Cumulative steps -  40818\n",
      "Reward -  140.0\n",
      "Loss -  0.04579237848520279\n",
      "\n",
      "\n",
      "Episode 1925 :\n",
      "Step -  741\n",
      "Cumulative steps -  41560\n",
      "Reward -  26.0\n",
      "Loss -  0.05028513818979263\n",
      "\n",
      "\n",
      "Episode 1926 :\n",
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      "Cumulative steps -  42556\n",
      "Reward -  68.0\n",
      "Loss -  0.08656607568264008\n",
      "\n",
      "\n",
      "Episode 1927 :\n",
      "Step -  1227\n",
      "Cumulative steps -  43784\n",
      "Reward -  33.0\n",
      "Loss -  0.027094628661870956\n",
      "\n",
      "\n",
      "Episode 1928 :\n",
      "Step -  856\n",
      "Cumulative steps -  44641\n",
      "Reward -  33.0\n",
      "Loss -  0.01683659851551056\n",
      "\n",
      "\n",
      "Episode 1929 :\n",
      "Step -  1976\n",
      "Cumulative steps -  46618\n",
      "Reward -  104.0\n",
      "Loss -  0.012755286879837513\n",
      "\n",
      "\n",
      "Episode 1930 :\n",
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      "Cumulative steps -  48646\n",
      "Reward -  132.0\n",
      "Loss -  0.02636120282113552\n",
      "\n",
      "\n",
      "Episode 1931 :\n",
      "Step -  1577\n",
      "Cumulative steps -  50224\n",
      "Reward -  35.0\n",
      "Loss -  0.056785937398672104\n",
      "\n",
      "\n",
      "Episode 1932 :\n",
      "Step -  1456\n",
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      "Reward -  89.0\n",
      "Loss -  0.02543802000582218\n",
      "\n",
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      "Episode 1933 :\n",
      "Step -  2247\n",
      "Cumulative steps -  53929\n",
      "Reward -  134.0\n",
      "Loss -  0.0779847726225853\n",
      "\n",
      "\n",
      "Episode 1934 :\n",
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      "Reward -  90.0\n",
      "Loss -  0.01954815909266472\n",
      "\n",
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      "Episode 1935 :\n",
      "Step -  3075\n",
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      "Reward -  167.0\n",
      "Loss -  0.0677996501326561\n",
      "\n",
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      "Episode 1936 :\n",
      "Step -  1425\n",
      "Cumulative steps -  60759\n",
      "Reward -  69.0\n",
      "Loss -  0.03900764882564545\n",
      "\n",
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      "Episode 1937 :\n",
      "Step -  932\n",
      "Cumulative steps -  61692\n",
      "Reward -  26.0\n",
      "Loss -  0.041153550148010254\n",
      "\n",
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      "Episode 1938 :\n",
      "Step -  1915\n",
      "Cumulative steps -  63608\n",
      "Reward -  104.0\n",
      "Loss -  0.04775762930512428\n",
      "\n",
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      "Episode 1939 :\n",
      "Step -  2710\n",
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      "Reward -  107.0\n",
      "Loss -  0.028658423572778702\n",
      "\n",
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      "Episode 1940 :\n",
      "Step -  2481\n",
      "Cumulative steps -  68801\n",
      "Reward -  137.0\n",
      "Loss -  0.029491398483514786\n",
      "\n",
      "\n",
      "Episode 1941 :\n",
      "Step -  2570\n",
      "Cumulative steps -  71372\n",
      "Reward -  75.0\n",
      "Loss -  0.059825778007507324\n",
      "\n",
      "\n",
      "Episode 1942 :\n",
      "Step -  1435\n",
      "Cumulative steps -  72808\n",
      "Reward -  88.0\n",
      "Loss -  0.07331150770187378\n",
      "\n",
      "\n",
      "Episode 1943 :\n",
      "Step -  2400\n",
      "Cumulative steps -  75209\n",
      "Reward -  140.0\n",
      "Loss -  0.044955551624298096\n",
      "\n",
      "\n",
      "Episode 1944 :\n",
      "Step -  3480\n",
      "Cumulative steps -  78690\n",
      "Reward -  184.0\n",
      "Loss -  0.03689692169427872\n",
      "\n",
      "\n",
      "Episode 1945 :\n",
      "Step -  1035\n",
      "Cumulative steps -  79726\n",
      "Reward -  63.0\n",
      "Loss -  0.04609083756804466\n",
      "\n",
      "\n",
      "Episode 1946 :\n",
      "Step -  2895\n",
      "Cumulative steps -  82622\n",
      "Reward -  209.0\n",
      "Loss -  0.028655722737312317\n",
      "\n",
      "\n",
      "Episode 1947 :\n",
      "Step -  2970\n",
      "Cumulative steps -  85593\n",
      "Reward -  184.0\n",
      "Loss -  0.029923003166913986\n",
      "\n",
      "\n",
      "Episode 1948 :\n",
      "Step -  1929\n",
      "Cumulative steps -  87523\n",
      "Reward -  113.0\n",
      "Loss -  0.027626674622297287\n",
      "\n",
      "\n",
      "Episode 1949 :\n",
      "Step -  1280\n",
      "Cumulative steps -  88804\n",
      "Reward -  59.0\n",
      "Loss -  0.03959476575255394\n",
      "\n",
      "\n",
      "Episode 1950 :\n",
      "Step -  3010\n",
      "Cumulative steps -  91815\n",
      "Reward -  180.0\n",
      "Loss -  0.07491549104452133\n",
      "\n",
      "\n",
      "Episode 1951 :\n",
      "Step -  2689\n",
      "Cumulative steps -  94505\n",
      "Reward -  170.0\n",
      "Loss -  0.013209700584411621\n",
      "\n",
      "\n",
      "Episode 1952 :\n",
      "Step -  774\n",
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      "Reward -  45.0\n",
      "Loss -  0.05335904657840729\n",
      "\n",
      "\n",
      "Episode 1953 :\n",
      "Step -  1782\n",
      "Cumulative steps -  97063\n",
      "Reward -  99.0\n",
      "Loss -  0.04984156787395477\n",
      "\n",
      "\n",
      "Episode 1954 :\n",
      "Step -  1086\n",
      "Cumulative steps -  98150\n",
      "Reward -  62.0\n",
      "Loss -  0.036619141697883606\n",
      "\n",
      "\n",
      "Episode 1955 :\n",
      "Step -  2555\n",
      "Cumulative steps -  100706\n",
      "Reward -  127.0\n",
      "Loss -  0.014800614677369595\n",
      "\n",
      "\n",
      "Episode 1956 :\n",
      "Step -  1774\n",
      "Cumulative steps -  102481\n",
      "Reward -  89.0\n",
      "Loss -  0.056458864361047745\n",
      "\n",
      "\n",
      "Episode 1957 :\n",
      "Step -  2685\n",
      "Cumulative steps -  105167\n",
      "Reward -  162.0\n",
      "Loss -  0.02938118949532509\n",
      "\n",
      "\n",
      "Episode 1958 :\n",
      "Step -  258\n",
      "Cumulative steps -  105426\n",
      "Reward -  13.0\n",
      "Loss -  0.03563451021909714\n",
      "\n",
      "\n",
      "Episode 1959 :\n",
      "Step -  2608\n",
      "Cumulative steps -  108035\n",
      "Reward -  118.0\n",
      "Loss -  0.11911247670650482\n",
      "\n",
      "\n",
      "Episode 1960 :\n",
      "Step -  1135\n",
      "Cumulative steps -  109171\n",
      "Reward -  56.0\n",
      "Loss -  0.045956119894981384\n",
      "\n",
      "\n",
      "Episode 1961 :\n",
      "Step -  1234\n",
      "Cumulative steps -  110406\n",
      "Reward -  87.0\n",
      "Loss -  0.06286382675170898\n",
      "\n",
      "\n",
      "Episode 1962 :\n",
      "Step -  3118\n",
      "Cumulative steps -  113525\n",
      "Reward -  206.0\n",
      "Loss -  0.6337818503379822\n",
      "\n",
      "\n",
      "Episode 1963 :\n",
      "Step -  1111\n",
      "Cumulative steps -  114637\n",
      "Reward -  58.0\n",
      "Loss -  0.0557657852768898\n",
      "\n",
      "\n",
      "Episode 1964 :\n",
      "Step -  1238\n",
      "Cumulative steps -  115876\n",
      "Reward -  66.0\n",
      "Loss -  0.03192669898271561\n",
      "\n",
      "\n",
      "Episode 1965 :\n",
      "Step -  1190\n",
      "Cumulative steps -  117067\n",
      "Reward -  62.0\n",
      "Loss -  0.022490093484520912\n",
      "\n",
      "\n",
      "Episode 1966 :\n",
      "Step -  2142\n",
      "Cumulative steps -  119210\n",
      "Reward -  158.0\n",
      "Loss -  0.03876011073589325\n",
      "\n",
      "\n",
      "Episode 1967 :\n",
      "Step -  1425\n",
      "Cumulative steps -  120636\n",
      "Reward -  106.0\n",
      "Loss -  0.029064465314149857\n",
      "\n",
      "\n",
      "Episode 1968 :\n",
      "Step -  1537\n",
      "Cumulative steps -  122174\n",
      "Reward -  68.0\n",
      "Loss -  0.119024857878685\n",
      "\n",
      "\n",
      "Episode 1969 :\n",
      "Step -  1822\n",
      "Cumulative steps -  123997\n",
      "Reward -  123.0\n",
      "Loss -  0.15451866388320923\n",
      "\n",
      "\n",
      "Episode 1970 :\n",
      "Step -  1321\n",
      "Cumulative steps -  125319\n",
      "Reward -  78.0\n",
      "Loss -  0.0427839495241642\n",
      "\n",
      "\n",
      "Episode 1971 :\n",
      "Step -  1109\n",
      "Cumulative steps -  126429\n",
      "Reward -  60.0\n",
      "Loss -  0.03470595180988312\n",
      "\n",
      "\n",
      "Episode 1972 :\n",
      "Step -  1602\n",
      "Cumulative steps -  128032\n",
      "Reward -  88.0\n",
      "Loss -  0.03666627034544945\n",
      "\n",
      "\n",
      "Episode 1973 :\n",
      "Step -  3426\n",
      "Cumulative steps -  131459\n",
      "Reward -  209.0\n",
      "Loss -  0.12361305952072144\n",
      "\n",
      "\n",
      "Episode 1974 :\n",
      "Step -  3405\n",
      "Cumulative steps -  134865\n",
      "Reward -  193.0\n",
      "Loss -  0.0371382050216198\n",
      "\n",
      "\n",
      "Episode 1975 :\n",
      "Step -  2638\n",
      "Cumulative steps -  137504\n",
      "Reward -  167.0\n",
      "Loss -  0.03936499357223511\n",
      "\n",
      "\n",
      "Episode 1976 :\n",
      "Step -  2230\n",
      "Cumulative steps -  139735\n",
      "Reward -  127.0\n",
      "Loss -  0.058450520038604736\n",
      "\n",
      "\n",
      "Episode 1977 :\n",
      "Step -  1226\n",
      "Cumulative steps -  140962\n",
      "Reward -  50.0\n",
      "Loss -  0.06972203403711319\n",
      "\n",
      "\n",
      "Episode 1978 :\n",
      "Step -  2002\n",
      "Cumulative steps -  142965\n",
      "Reward -  135.0\n",
      "Loss -  0.057131774723529816\n",
      "\n",
      "\n",
      "Episode 1979 :\n",
      "Step -  2416\n",
      "Cumulative steps -  145382\n",
      "Reward -  142.0\n",
      "Loss -  0.051171619445085526\n",
      "\n",
      "\n",
      "Episode 1980 :\n",
      "Step -  3507\n",
      "Cumulative steps -  148890\n",
      "Reward -  205.0\n",
      "Loss -  0.03572347015142441\n",
      "\n",
      "\n",
      "Episode 1981 :\n",
      "Step -  2665\n",
      "Cumulative steps -  151556\n",
      "Reward -  62.0\n",
      "Loss -  0.035974763333797455\n",
      "\n",
      "\n",
      "Episode 1982 :\n",
      "Step -  2101\n",
      "Cumulative steps -  153658\n",
      "Reward -  99.0\n",
      "Loss -  0.04607555270195007\n",
      "\n",
      "\n",
      "Episode 1983 :\n",
      "Step -  1429\n",
      "Cumulative steps -  155088\n",
      "Reward -  75.0\n",
      "Loss -  0.039278753101825714\n",
      "\n",
      "\n",
      "Episode 1984 :\n",
      "Step -  2327\n",
      "Cumulative steps -  157416\n",
      "Reward -  145.0\n",
      "Loss -  0.04289943352341652\n",
      "\n",
      "\n",
      "Episode 1985 :\n",
      "Step -  3038\n",
      "Cumulative steps -  160455\n",
      "Reward -  210.0\n",
      "Loss -  0.055761754512786865\n",
      "\n",
      "\n",
      "Episode 1986 :\n",
      "Step -  2352\n",
      "Cumulative steps -  162808\n",
      "Reward -  135.0\n",
      "Loss -  0.07322850823402405\n",
      "\n",
      "\n",
      "Episode 1987 :\n",
      "Step -  3838\n",
      "Cumulative steps -  166647\n",
      "Reward -  303.0\n",
      "Loss -  0.039997417479753494\n",
      "\n",
      "\n",
      "Episode 1988 :\n",
      "Step -  3537\n",
      "Cumulative steps -  170185\n",
      "Reward -  242.0\n",
      "Loss -  0.06415246427059174\n",
      "\n",
      "\n",
      "Episode 1989 :\n",
      "Step -  2702\n",
      "Cumulative steps -  172888\n",
      "Reward -  207.0\n",
      "Loss -  0.06141502782702446\n",
      "\n",
      "\n",
      "Episode 1990 :\n",
      "Step -  5252\n",
      "Cumulative steps -  178141\n",
      "Reward -  333.0\n",
      "Loss -  0.06551744043827057\n",
      "\n",
      "\n",
      "Episode 1991 :\n",
      "Step -  1991\n",
      "Cumulative steps -  180133\n",
      "Reward -  135.0\n",
      "Loss -  0.03249814361333847\n",
      "\n",
      "\n",
      "Episode 1992 :\n",
      "Step -  1501\n",
      "Cumulative steps -  181635\n",
      "Reward -  54.0\n",
      "Loss -  0.035482410341501236\n",
      "\n",
      "\n",
      "Episode 1993 :\n",
      "Step -  5222\n",
      "Cumulative steps -  186858\n",
      "Reward -  187.0\n",
      "Loss -  0.04029778391122818\n",
      "\n",
      "\n",
      "Episode 1994 :\n",
      "Step -  2300\n",
      "Cumulative steps -  189159\n",
      "Reward -  119.0\n",
      "Loss -  0.042626477777957916\n",
      "\n",
      "\n",
      "Episode 1995 :\n",
      "Step -  2682\n",
      "Cumulative steps -  191842\n",
      "Reward -  132.0\n",
      "Loss -  0.053470827639102936\n",
      "\n",
      "\n",
      "Episode 1996 :\n",
      "Step -  3111\n",
      "Cumulative steps -  194954\n",
      "Reward -  197.0\n",
      "Loss -  0.042339663952589035\n",
      "\n",
      "\n",
      "Episode 1997 :\n",
      "Step -  884\n",
      "Cumulative steps -  195839\n",
      "Reward -  12.0\n",
      "Loss -  0.03110346570611\n",
      "\n",
      "\n",
      "Episode 1998 :\n",
      "Step -  1526\n",
      "Cumulative steps -  197366\n",
      "Reward -  91.0\n",
      "Loss -  0.04949191212654114\n",
      "\n",
      "\n",
      "Episode 1999 :\n",
      "Step -  2500\n",
      "Cumulative steps -  199867\n",
      "Reward -  107.0\n",
      "Loss -  0.049871690571308136\n",
      "\n",
      "\n",
      "Episode 2000 :\n",
      "Step -  1750\n",
      "Cumulative steps -  201618\n",
      "Reward -  73.0\n",
      "Loss -  0.07935737073421478\n",
      "\n",
      "\n",
      "Episode 2001 :\n",
      "Step -  979\n",
      "Cumulative steps -  202598\n",
      "Reward -  34.0\n",
      "Loss -  0.045132577419281006\n",
      "\n",
      "\n",
      "Episode 2002 :\n",
      "Step -  2318\n",
      "Cumulative steps -  204917\n",
      "Reward -  122.0\n",
      "Loss -  0.043679215013980865\n",
      "\n",
      "\n",
      "Episode 2003 :\n",
      "Step -  944\n",
      "Cumulative steps -  205862\n",
      "Reward -  64.0\n",
      "Loss -  0.025831399485468864\n",
      "\n",
      "\n",
      "Episode 2004 :\n",
      "Step -  1600\n",
      "Cumulative steps -  207463\n",
      "Reward -  20.0\n",
      "Loss -  0.023856360465288162\n",
      "\n",
      "\n",
      "Episode 2005 :\n",
      "Step -  1404\n",
      "Cumulative steps -  208868\n",
      "Reward -  76.0\n",
      "Loss -  0.05307377502322197\n",
      "\n",
      "\n",
      "Episode 2006 :\n",
      "Step -  990\n",
      "Cumulative steps -  209859\n",
      "Reward -  57.0\n",
      "Loss -  0.04904208332300186\n",
      "\n",
      "\n",
      "Episode 2007 :\n",
      "Step -  4221\n",
      "Cumulative steps -  214081\n",
      "Reward -  280.0\n",
      "Loss -  0.02782702073454857\n",
      "\n",
      "\n",
      "Episode 2008 :\n",
      "Step -  4430\n",
      "Cumulative steps -  218512\n",
      "Reward -  258.0\n",
      "Loss -  0.03665903955698013\n",
      "\n",
      "\n",
      "Episode 2009 :\n",
      "Step -  3941\n",
      "Cumulative steps -  222454\n",
      "Reward -  236.0\n",
      "Loss -  0.042816296219825745\n",
      "\n",
      "\n",
      "Episode 2010 :\n",
      "Step -  3023\n",
      "Cumulative steps -  225478\n",
      "Reward -  186.0\n",
      "Loss -  0.07544384896755219\n",
      "\n",
      "\n",
      "Episode 2011 :\n",
      "Step -  2748\n",
      "Cumulative steps -  228227\n",
      "Reward -  203.0\n",
      "Loss -  0.03695238381624222\n",
      "\n",
      "\n",
      "Episode 2012 :\n",
      "Step -  1069\n",
      "Cumulative steps -  229297\n",
      "Reward -  61.0\n",
      "Loss -  0.023931676521897316\n",
      "\n",
      "\n",
      "Episode 2013 :\n",
      "Step -  4336\n",
      "Cumulative steps -  233634\n",
      "Reward -  286.0\n",
      "Loss -  0.01521579921245575\n",
      "\n",
      "\n",
      "Episode 2014 :\n",
      "Step -  2592\n",
      "Cumulative steps -  236227\n",
      "Reward -  194.0\n",
      "Loss -  0.0374208502471447\n",
      "\n",
      "\n",
      "Episode 2015 :\n",
      "Step -  3683\n",
      "Cumulative steps -  239911\n",
      "Reward -  239.0\n",
      "Loss -  0.046662189066410065\n",
      "\n",
      "\n",
      "Episode 2016 :\n",
      "Step -  2484\n",
      "Cumulative steps -  242396\n",
      "Reward -  159.0\n",
      "Loss -  0.04588984698057175\n",
      "\n",
      "\n",
      "Episode 2017 :\n",
      "Step -  1470\n",
      "Cumulative steps -  243867\n",
      "Reward -  83.0\n",
      "Loss -  0.060183923691511154\n",
      "\n",
      "\n",
      "Episode 2018 :\n",
      "Step -  679\n",
      "Cumulative steps -  244547\n",
      "Reward -  39.0\n",
      "Loss -  0.03250405192375183\n",
      "\n",
      "\n",
      "Episode 2019 :\n",
      "Step -  1961\n",
      "Cumulative steps -  246509\n",
      "Reward -  104.0\n",
      "Loss -  0.7011286020278931\n",
      "\n",
      "\n",
      "Episode 2020 :\n",
      "Step -  1154\n",
      "Cumulative steps -  247664\n",
      "Reward -  79.0\n",
      "Loss -  0.046442028135061264\n",
      "\n",
      "\n",
      "Episode 2021 :\n",
      "Step -  4101\n",
      "Cumulative steps -  251766\n",
      "Reward -  282.0\n",
      "Loss -  0.05784861370921135\n",
      "\n",
      "\n",
      "Episode 2022 :\n",
      "Step -  3797\n",
      "Cumulative steps -  255564\n",
      "Reward -  249.0\n",
      "Loss -  0.06574572622776031\n",
      "\n",
      "\n",
      "Episode 2023 :\n",
      "Step -  1565\n",
      "Cumulative steps -  257130\n",
      "Reward -  62.0\n",
      "Loss -  0.04638121649622917\n",
      "\n",
      "\n",
      "Episode 2024 :\n",
      "Step -  2642\n",
      "Cumulative steps -  259773\n",
      "Reward -  150.0\n",
      "Loss -  0.03020097129046917\n",
      "\n",
      "\n",
      "Episode 2025 :\n",
      "Step -  1315\n",
      "Cumulative steps -  261089\n",
      "Reward -  74.0\n",
      "Loss -  0.05046962574124336\n",
      "\n",
      "\n",
      "Episode 2026 :\n",
      "Step -  6363\n",
      "Cumulative steps -  267453\n",
      "Reward -  411.0\n",
      "Loss -  0.05734054744243622\n",
      "\n",
      "\n",
      "Episode 2027 :\n",
      "Step -  2899\n",
      "Cumulative steps -  270353\n",
      "Reward -  164.0\n",
      "Loss -  0.05196116119623184\n",
      "\n",
      "\n",
      "Episode 2028 :\n",
      "Step -  3546\n",
      "Cumulative steps -  273900\n",
      "Reward -  181.0\n",
      "Loss -  0.04999513924121857\n",
      "\n",
      "\n",
      "Episode 2029 :\n",
      "Step -  2274\n",
      "Cumulative steps -  276175\n",
      "Reward -  164.0\n",
      "Loss -  0.045003652572631836\n",
      "\n",
      "\n",
      "Episode 2030 :\n",
      "Step -  3886\n",
      "Cumulative steps -  280062\n",
      "Reward -  270.0\n",
      "Loss -  0.03997102007269859\n",
      "\n",
      "\n",
      "Episode 2031 :\n",
      "Step -  2246\n",
      "Cumulative steps -  282309\n",
      "Reward -  163.0\n",
      "Loss -  0.024160422384738922\n",
      "\n",
      "\n",
      "Episode 2032 :\n",
      "Step -  3471\n",
      "Cumulative steps -  285781\n",
      "Reward -  250.0\n",
      "Loss -  0.03100178763270378\n",
      "\n",
      "\n",
      "Episode 2033 :\n",
      "Step -  1003\n",
      "Cumulative steps -  286785\n",
      "Reward -  71.0\n",
      "Loss -  0.042116403579711914\n",
      "\n",
      "\n",
      "Episode 2034 :\n",
      "Step -  1097\n",
      "Cumulative steps -  287883\n",
      "Reward -  48.0\n",
      "Loss -  0.06746715307235718\n",
      "\n",
      "\n",
      "Episode 2035 :\n",
      "Step -  4257\n",
      "Cumulative steps -  292141\n",
      "Reward -  269.0\n",
      "Loss -  0.07724110037088394\n",
      "\n",
      "\n",
      "Episode 2036 :\n",
      "Step -  2295\n",
      "Cumulative steps -  294437\n",
      "Reward -  134.0\n",
      "Loss -  0.045670460909605026\n",
      "\n",
      "\n",
      "Episode 2037 :\n",
      "Step -  3438\n",
      "Cumulative steps -  297876\n",
      "Reward -  179.0\n",
      "Loss -  0.05021628737449646\n",
      "\n",
      "\n",
      "Episode 2038 :\n",
      "Step -  2071\n",
      "Cumulative steps -  299948\n",
      "Reward -  98.0\n",
      "Loss -  0.040510766208171844\n",
      "\n",
      "\n",
      "Episode 2039 :\n",
      "Step -  5129\n",
      "Cumulative steps -  305078\n",
      "Reward -  380.0\n",
      "Loss -  0.06355397403240204\n",
      "\n",
      "\n",
      "Episode 2040 :\n",
      "Step -  3396\n",
      "Cumulative steps -  308475\n",
      "Reward -  236.0\n",
      "Loss -  0.0494396835565567\n",
      "\n",
      "\n",
      "Episode 2041 :\n",
      "Step -  1481\n",
      "Cumulative steps -  309957\n",
      "Reward -  100.0\n",
      "Loss -  0.041189808398485184\n",
      "\n",
      "\n",
      "Episode 2042 :\n",
      "Step -  1158\n",
      "Cumulative steps -  311116\n",
      "Reward -  41.0\n",
      "Loss -  0.055668529123067856\n",
      "\n",
      "\n",
      "Episode 2043 :\n",
      "Step -  3815\n",
      "Cumulative steps -  314932\n",
      "Reward -  283.0\n",
      "Loss -  0.0427132286131382\n",
      "\n",
      "\n",
      "Episode 2044 :\n",
      "Step -  2773\n",
      "Cumulative steps -  317706\n",
      "Reward -  150.0\n",
      "Loss -  0.05028920993208885\n",
      "\n",
      "\n",
      "Episode 2045 :\n",
      "Step -  1641\n",
      "Cumulative steps -  319348\n",
      "Reward -  81.0\n",
      "Loss -  0.03460539132356644\n",
      "\n",
      "\n",
      "Episode 2046 :\n",
      "Step -  2647\n",
      "Cumulative steps -  321996\n",
      "Reward -  152.0\n",
      "Loss -  0.07417885959148407\n",
      "\n",
      "\n",
      "Episode 2047 :\n",
      "Step -  2273\n",
      "Cumulative steps -  324270\n",
      "Reward -  135.0\n",
      "Loss -  0.05327785760164261\n",
      "\n",
      "\n",
      "Episode 2048 :\n",
      "Step -  1855\n",
      "Cumulative steps -  326126\n",
      "Reward -  106.0\n",
      "Loss -  0.025809185579419136\n",
      "\n",
      "\n",
      "Episode 2049 :\n",
      "Step -  1571\n",
      "Cumulative steps -  327698\n",
      "Reward -  118.0\n",
      "Loss -  0.04196906089782715\n",
      "\n",
      "\n",
      "Episode 2050 :\n",
      "Step -  3047\n",
      "Cumulative steps -  330746\n",
      "Reward -  216.0\n",
      "Loss -  0.07541657984256744\n",
      "\n",
      "\n",
      "Episode 2051 :\n",
      "Step -  1962\n",
      "Cumulative steps -  332709\n",
      "Reward -  152.0\n",
      "Loss -  0.03898417949676514\n",
      "\n",
      "\n",
      "Episode 2052 :\n",
      "Step -  2294\n",
      "Cumulative steps -  335004\n",
      "Reward -  156.0\n",
      "Loss -  0.04021181911230087\n",
      "\n",
      "\n",
      "Episode 2053 :\n",
      "Step -  4042\n",
      "Cumulative steps -  339047\n",
      "Reward -  270.0\n",
      "Loss -  0.028899461030960083\n",
      "\n",
      "\n",
      "Episode 2054 :\n",
      "Step -  8003\n",
      "Cumulative steps -  347051\n",
      "Reward -  578.0\n",
      "Loss -  0.05981146916747093\n",
      "\n",
      "\n",
      "Episode 2055 :\n",
      "Step -  1388\n",
      "Cumulative steps -  348440\n",
      "Reward -  104.0\n",
      "Loss -  0.07456228882074356\n",
      "\n",
      "\n",
      "Episode 2056 :\n",
      "Step -  7818\n",
      "Cumulative steps -  356259\n",
      "Reward -  610.0\n",
      "Loss -  0.06235773116350174\n",
      "\n",
      "\n",
      "Episode 2057 :\n",
      "Step -  5108\n",
      "Cumulative steps -  361368\n",
      "Reward -  261.0\n",
      "Loss -  0.07971493899822235\n",
      "\n",
      "\n",
      "Episode 2058 :\n",
      "Step -  1017\n",
      "Cumulative steps -  362386\n",
      "Reward -  70.0\n",
      "Loss -  0.06975158303976059\n",
      "\n",
      "\n",
      "Episode 2059 :\n",
      "Step -  5524\n",
      "Cumulative steps -  367911\n",
      "Reward -  369.0\n",
      "Loss -  0.029341725632548332\n",
      "\n",
      "\n",
      "Episode 2060 :\n",
      "Step -  1423\n",
      "Cumulative steps -  369335\n",
      "Reward -  85.0\n",
      "Loss -  0.03743167966604233\n",
      "\n",
      "\n",
      "Episode 2061 :\n",
      "Step -  2136\n",
      "Cumulative steps -  371472\n",
      "Reward -  128.0\n",
      "Loss -  0.032746005803346634\n",
      "\n",
      "\n",
      "Episode 2062 :\n",
      "Step -  3367\n",
      "Cumulative steps -  374840\n",
      "Reward -  266.0\n",
      "Loss -  0.03810266777873039\n",
      "\n",
      "\n",
      "Episode 2063 :\n",
      "Step -  5626\n",
      "Cumulative steps -  380467\n",
      "Reward -  387.0\n",
      "Loss -  0.05082285776734352\n",
      "\n",
      "\n",
      "Episode 2064 :\n",
      "Step -  3101\n",
      "Cumulative steps -  383569\n",
      "Reward -  236.0\n",
      "Loss -  0.05600447207689285\n",
      "\n",
      "\n",
      "Episode 2065 :\n",
      "Step -  4397\n",
      "Cumulative steps -  387967\n",
      "Reward -  287.0\n",
      "Loss -  0.0861673504114151\n",
      "\n",
      "\n",
      "Episode 2066 :\n",
      "Step -  1039\n",
      "Cumulative steps -  389007\n",
      "Reward -  46.0\n",
      "Loss -  0.05286235734820366\n",
      "\n",
      "\n",
      "Episode 2067 :\n",
      "Step -  1421\n",
      "Cumulative steps -  390429\n",
      "Reward -  88.0\n",
      "Loss -  0.07804390788078308\n",
      "\n",
      "\n",
      "Episode 2068 :\n",
      "Step -  3013\n",
      "Cumulative steps -  393443\n",
      "Reward -  181.0\n",
      "Loss -  0.061667513102293015\n",
      "\n",
      "\n",
      "Episode 2069 :\n",
      "Step -  2056\n",
      "Cumulative steps -  395500\n",
      "Reward -  143.0\n",
      "Loss -  0.04786164313554764\n",
      "\n",
      "\n",
      "Episode 2070 :\n",
      "Step -  1310\n",
      "Cumulative steps -  396811\n",
      "Reward -  31.0\n",
      "Loss -  0.06842078268527985\n",
      "\n",
      "\n",
      "Episode 2071 :\n",
      "Step -  1928\n",
      "Cumulative steps -  398740\n",
      "Reward -  168.0\n",
      "Loss -  0.10279873013496399\n",
      "\n",
      "\n",
      "Episode 2072 :\n",
      "Step -  1791\n",
      "Cumulative steps -  400532\n",
      "Reward -  137.0\n",
      "Loss -  0.05586744472384453\n",
      "\n",
      "\n",
      "Episode 2073 :\n",
      "Step -  1332\n",
      "Cumulative steps -  401865\n",
      "Reward -  57.0\n",
      "Loss -  0.04551265388727188\n",
      "\n",
      "\n",
      "Episode 2074 :\n",
      "Step -  2162\n",
      "Cumulative steps -  404028\n",
      "Reward -  146.0\n",
      "Loss -  0.04184623435139656\n",
      "\n",
      "\n",
      "Episode 2075 :\n",
      "Step -  1102\n",
      "Cumulative steps -  405131\n",
      "Reward -  64.0\n",
      "Loss -  0.050970569252967834\n",
      "\n",
      "\n",
      "Episode 2076 :\n",
      "Step -  2027\n",
      "Cumulative steps -  407159\n",
      "Reward -  157.0\n",
      "Loss -  0.08387233316898346\n",
      "\n",
      "\n",
      "Episode 2077 :\n",
      "Step -  1833\n",
      "Cumulative steps -  408993\n",
      "Reward -  119.0\n",
      "Loss -  0.0731019526720047\n",
      "\n",
      "\n",
      "Episode 2078 :\n",
      "Step -  3322\n",
      "Cumulative steps -  412316\n",
      "Reward -  272.0\n",
      "Loss -  0.07492293417453766\n",
      "\n",
      "\n",
      "Episode 2079 :\n",
      "Step -  1219\n",
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      "Reward -  79.0\n",
      "Loss -  0.0537327378988266\n",
      "\n",
      "\n",
      "Episode 2080 :\n",
      "Step -  3517\n",
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      "Reward -  208.0\n",
      "Loss -  0.04177558422088623\n",
      "\n",
      "\n",
      "Episode 2081 :\n",
      "Step -  5856\n",
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      "Reward -  421.0\n",
      "Loss -  0.12891699373722076\n",
      "\n",
      "\n",
      "Episode 2082 :\n",
      "Step -  2288\n",
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      "Reward -  146.0\n",
      "Loss -  0.0423339381814003\n",
      "\n",
      "\n",
      "Episode 2083 :\n",
      "Step -  3544\n",
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      "Reward -  261.0\n",
      "Loss -  0.08051501214504242\n",
      "\n",
      "\n",
      "Episode 2084 :\n",
      "Step -  4065\n",
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      "Reward -  216.0\n",
      "Loss -  0.05714554339647293\n",
      "\n",
      "\n",
      "Episode 2085 :\n",
      "Step -  3152\n",
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      "Reward -  197.0\n",
      "Loss -  0.055843815207481384\n",
      "\n",
      "\n",
      "Episode 2086 :\n",
      "Step -  1348\n",
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      "Reward -  96.0\n",
      "Loss -  0.03765217214822769\n",
      "\n",
      "\n",
      "Episode 2087 :\n",
      "Step -  1528\n",
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      "Reward -  113.0\n",
      "Loss -  0.09862428903579712\n",
      "\n",
      "\n",
      "Episode 2088 :\n",
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      "Reward -  251.0\n",
      "Loss -  0.09107126295566559\n",
      "\n",
      "\n",
      "Episode 2089 :\n",
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      "Reward -  180.0\n",
      "Loss -  0.04180511087179184\n",
      "\n",
      "\n",
      "Episode 2090 :\n",
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      "Reward -  317.0\n",
      "Loss -  0.13078166544437408\n",
      "\n",
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      "Episode 2091 :\n",
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      "Reward -  248.0\n",
      "Loss -  0.12528914213180542\n",
      "\n",
      "\n",
      "Episode 2092 :\n",
      "Step -  2016\n",
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      "Reward -  140.0\n",
      "Loss -  0.055571168661117554\n",
      "\n",
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      "Episode 2093 :\n",
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      "Reward -  28.0\n",
      "Loss -  0.05059988051652908\n",
      "\n",
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      "Episode 2094 :\n",
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      "Reward -  173.0\n",
      "Loss -  0.05261935293674469\n",
      "\n",
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      "Episode 2095 :\n",
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      "Reward -  114.0\n",
      "Loss -  0.05574846640229225\n",
      "\n",
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      "Episode 2096 :\n",
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      "Reward -  507.0\n",
      "Loss -  0.04490680992603302\n",
      "\n",
      "\n",
      "Episode 2097 :\n",
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      "Reward -  40.0\n",
      "Loss -  0.05215734988451004\n",
      "\n",
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      "Episode 2098 :\n",
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      "Reward -  161.0\n",
      "Loss -  0.02236142009496689\n",
      "\n",
      "\n",
      "Episode 2099 :\n",
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      "Reward -  345.0\n",
      "Loss -  0.06258267164230347\n",
      "\n",
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      "Episode 2100 :\n",
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      "Reward -  236.0\n",
      "Loss -  0.07778629660606384\n",
      "\n",
      "\n",
      "Episode 2101 :\n",
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      "Reward -  236.0\n",
      "Loss -  0.049885764718055725\n",
      "\n",
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      "Episode 2102 :\n",
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      "Reward -  179.0\n",
      "Loss -  0.05476653203368187\n",
      "\n",
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      "Episode 2103 :\n",
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      "Reward -  163.0\n",
      "Loss -  0.06347507983446121\n",
      "\n",
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      "Episode 2104 :\n",
      "Step -  2000\n",
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      "Reward -  117.0\n",
      "Loss -  0.041985884308815\n",
      "\n",
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      "Episode 2105 :\n",
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      "Reward -  135.0\n",
      "Loss -  0.05299267917871475\n",
      "\n",
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      "Episode 2106 :\n",
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      "Reward -  113.0\n",
      "Loss -  0.07301557809114456\n",
      "\n",
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      "Episode 2107 :\n",
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      "Reward -  271.0\n",
      "Loss -  0.08521157503128052\n",
      "\n",
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      "Episode 2108 :\n",
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      "Reward -  156.0\n",
      "Loss -  0.08825059980154037\n",
      "\n",
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      "Episode 2109 :\n",
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      "Reward -  121.0\n",
      "Loss -  0.09697800874710083\n",
      "\n",
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      "Episode 2110 :\n",
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      "Reward -  324.0\n",
      "Loss -  0.05259258672595024\n",
      "\n",
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      "Episode 2111 :\n",
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      "Reward -  36.0\n",
      "Loss -  0.04624957963824272\n",
      "\n",
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      "Episode 2112 :\n",
      "Step -  3820\n",
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      "Reward -  188.0\n",
      "Loss -  0.029558153823018074\n",
      "\n",
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      "Episode 2113 :\n",
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      "Reward -  176.0\n",
      "Loss -  0.05451657623052597\n",
      "\n",
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      "Episode 2114 :\n",
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      "Reward -  106.0\n",
      "Loss -  0.07648200541734695\n",
      "\n",
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      "Episode 2115 :\n",
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      "Reward -  96.0\n",
      "Loss -  0.05143515020608902\n",
      "\n",
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      "Episode 2116 :\n",
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      "Reward -  175.0\n",
      "Loss -  0.08350943773984909\n",
      "\n",
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      "Episode 2117 :\n",
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      "Reward -  82.0\n",
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      "\n",
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      "Episode 2118 :\n",
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      "Reward -  142.0\n",
      "Loss -  0.032935600727796555\n",
      "\n",
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      "Episode 2119 :\n",
      "Step -  3934\n",
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      "Reward -  207.0\n",
      "Loss -  0.04576117545366287\n",
      "\n",
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      "Episode 2120 :\n",
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      "Reward -  398.0\n",
      "Loss -  0.029761776328086853\n",
      "\n",
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      "Episode 2121 :\n",
      "Step -  2726\n",
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      "Reward -  207.0\n",
      "Loss -  0.05638526380062103\n",
      "\n",
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      "Episode 2122 :\n",
      "Step -  4509\n",
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      "Reward -  260.0\n",
      "Loss -  0.043872933834791183\n",
      "\n",
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      "Episode 2123 :\n",
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      "Reward -  279.0\n",
      "Loss -  0.09676044434309006\n",
      "\n",
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      "Episode 2124 :\n",
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      "Reward -  71.0\n",
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      "\n",
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      "Episode 2125 :\n",
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      "Reward -  91.0\n",
      "Loss -  0.046838510781526566\n",
      "\n",
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      "Episode 2126 :\n",
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      "Reward -  173.0\n",
      "Loss -  0.05253283679485321\n",
      "\n",
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      "Episode 2127 :\n",
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      "Reward -  56.0\n",
      "Loss -  0.04950264096260071\n",
      "\n",
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      "Episode 2128 :\n",
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      "Reward -  334.0\n",
      "Loss -  0.07456359267234802\n",
      "\n",
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      "Episode 2129 :\n",
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      "Reward -  56.0\n",
      "Loss -  0.0802263617515564\n",
      "\n",
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      "Episode 2130 :\n",
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      "Reward -  103.0\n",
      "Loss -  0.02642308548092842\n",
      "\n",
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      "Episode 2131 :\n",
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      "Reward -  92.0\n",
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      "Episode 2132 :\n",
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      "Reward -  169.0\n",
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      "\n",
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      "Episode 2133 :\n",
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      "Reward -  383.0\n",
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      "\n",
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      "Episode 2134 :\n",
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      "Reward -  230.0\n",
      "Loss -  0.06240351125597954\n",
      "\n",
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      "Episode 2135 :\n",
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      "Reward -  185.0\n",
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      "\n",
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      "Episode 2136 :\n",
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      "Reward -  66.0\n",
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      "\n",
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      "Episode 2137 :\n",
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      "Reward -  115.0\n",
      "Loss -  0.05704828351736069\n",
      "\n",
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      "Episode 2138 :\n",
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      "\n",
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      "Episode 2139 :\n",
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      "\n",
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      "Episode 2140 :\n",
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      "Reward -  343.0\n",
      "Loss -  0.061969440430402756\n",
      "\n",
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      "Episode 2141 :\n",
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      "Reward -  186.0\n",
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      "Episode 2142 :\n",
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      "Reward -  73.0\n",
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      "Episode 2143 :\n",
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      "Episode 2144 :\n",
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      "Episode 2145 :\n",
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      "Episode 2146 :\n",
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      "Reward -  81.0\n",
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      "Episode 2147 :\n",
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      "Episode 2148 :\n",
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      "Episode 2149 :\n",
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      "Episode 2150 :\n",
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      "Episode 2151 :\n",
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      "Episode 2152 :\n",
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      "Loss -  5.610777952824719e-05\n",
      "\n",
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      "Episode 2153 :\n",
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      "Reward -  6.0\n",
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      "Episode 2154 :\n",
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      "Episode 2155 :\n",
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      "Episode 2156 :\n",
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      "Reward -  78.0\n",
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      "Episode 2157 :\n",
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      "Episode 2158 :\n",
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      "Episode 2159 :\n",
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      "Reward -  8.0\n",
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      "Episode 2160 :\n",
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      "Episode 2161 :\n",
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      "Cumulative steps -  293123\n",
      "Reward -  23.0\n",
      "Loss -  0.0200975239276886\n",
      "\n",
      "\n",
      "Episode 2176 :\n",
      "Step -  2045\n",
      "Cumulative steps -  295169\n",
      "Reward -  82.0\n",
      "Loss -  0.030753642320632935\n",
      "\n",
      "\n",
      "Episode 2177 :\n",
      "Step -  1939\n",
      "Cumulative steps -  297109\n",
      "Reward -  120.0\n",
      "Loss -  0.05858950689435005\n",
      "\n",
      "\n",
      "Episode 2178 :\n",
      "Step -  3092\n",
      "Cumulative steps -  300202\n",
      "Reward -  206.0\n",
      "Loss -  0.021501019597053528\n",
      "\n",
      "\n",
      "Episode 2179 :\n",
      "Step -  2838\n",
      "Cumulative steps -  303041\n",
      "Reward -  171.0\n",
      "Loss -  0.14320522546768188\n",
      "\n",
      "\n",
      "Episode 2180 :\n",
      "Step -  1592\n",
      "Cumulative steps -  304634\n",
      "Reward -  39.0\n",
      "Loss -  0.024423295632004738\n",
      "\n",
      "\n",
      "Episode 2181 :\n",
      "Step -  897\n",
      "Cumulative steps -  305532\n",
      "Reward -  58.0\n",
      "Loss -  0.11246687173843384\n",
      "\n",
      "\n",
      "Episode 2182 :\n",
      "Step -  1070\n",
      "Cumulative steps -  306603\n",
      "Reward -  29.0\n",
      "Loss -  0.033274248242378235\n",
      "\n",
      "\n",
      "Episode 2183 :\n",
      "Step -  1513\n",
      "Cumulative steps -  308117\n",
      "Reward -  79.0\n",
      "Loss -  0.032437555491924286\n",
      "\n",
      "\n",
      "Episode 2184 :\n",
      "Step -  304\n",
      "Cumulative steps -  308422\n",
      "Reward -  7.0\n",
      "Loss -  0.02044413983821869\n",
      "\n",
      "\n",
      "Episode 2185 :\n",
      "Step -  1567\n",
      "Cumulative steps -  309990\n",
      "Reward -  80.0\n",
      "Loss -  0.03314054757356644\n",
      "\n",
      "\n",
      "Episode 2186 :\n",
      "Step -  2162\n",
      "Cumulative steps -  312153\n",
      "Reward -  120.0\n",
      "Loss -  0.01750282384455204\n",
      "\n",
      "\n",
      "Episode 2187 :\n",
      "Step -  2119\n",
      "Cumulative steps -  314273\n",
      "Reward -  120.0\n",
      "Loss -  0.05369812250137329\n",
      "\n",
      "\n",
      "Episode 2188 :\n",
      "Step -  2969\n",
      "Cumulative steps -  317243\n",
      "Reward -  173.0\n",
      "Loss -  0.019865654408931732\n",
      "\n",
      "\n",
      "Episode 2189 :\n",
      "Step -  2155\n",
      "Cumulative steps -  319399\n",
      "Reward -  153.0\n",
      "Loss -  0.03982682153582573\n",
      "\n",
      "\n",
      "Episode 2190 :\n",
      "Step -  3482\n",
      "Cumulative steps -  322882\n",
      "Reward -  159.0\n",
      "Loss -  0.0670439600944519\n",
      "\n",
      "\n",
      "Episode 2191 :\n",
      "Step -  2824\n",
      "Cumulative steps -  325707\n",
      "Reward -  184.0\n",
      "Loss -  0.03064209222793579\n",
      "\n",
      "\n",
      "Episode 2192 :\n",
      "Step -  1626\n",
      "Cumulative steps -  327334\n",
      "Reward -  100.0\n",
      "Loss -  0.038907188922166824\n",
      "\n",
      "\n",
      "Episode 2193 :\n",
      "Step -  777\n",
      "Cumulative steps -  328112\n",
      "Reward -  37.0\n",
      "Loss -  0.03323742002248764\n",
      "\n",
      "\n",
      "Episode 2194 :\n",
      "Step -  1857\n",
      "Cumulative steps -  329970\n",
      "Reward -  144.0\n",
      "Loss -  0.04670047014951706\n",
      "\n",
      "\n",
      "Episode 2195 :\n",
      "Step -  3549\n",
      "Cumulative steps -  333520\n",
      "Reward -  225.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss -  0.02592669241130352\n",
      "\n",
      "\n",
      "Episode 2196 :\n",
      "Step -  3335\n",
      "Cumulative steps -  336856\n",
      "Reward -  169.0\n",
      "Loss -  0.05711923539638519\n",
      "\n",
      "\n",
      "Episode 2197 :\n",
      "Step -  1306\n",
      "Cumulative steps -  338163\n",
      "Reward -  52.0\n",
      "Loss -  0.05691765621304512\n",
      "\n",
      "\n",
      "Episode 2198 :\n",
      "Step -  878\n",
      "Cumulative steps -  339042\n",
      "Reward -  47.0\n",
      "Loss -  0.10071975737810135\n",
      "\n",
      "\n",
      "Episode 2199 :\n",
      "Step -  1251\n",
      "Cumulative steps -  340294\n",
      "Reward -  63.0\n",
      "Loss -  0.019598251208662987\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if 'metrics' not in locals():\n",
    "    import pickle\n",
    "    with open('models/VideoPinballNoFrameskip-v4_10000.metrics', 'rb') as metrics_file:\n",
    "        metrics = pickle.load(metrics_file)\n",
    "\n",
    "rewards = metrics['rewards']\n",
    "losses = metrics['losses']\n",
    "steps = metrics['steps']\n",
    "cum_steps = metrics['cum_steps']\n",
    "\n",
    "assert len(rewards) == len(losses) == len(steps)\n",
    "\n",
    "episodes_info = list(zip(rewards, losses, steps, cum_steps))\n",
    "for i, (reward, loss, step, cum_step) in enumerate(episodes_info):\n",
    "    print('Episode %d :' % i)\n",
    "    print('Step - ', step)\n",
    "    print('Cumulative steps - ', cum_step)\n",
    "    print('Reward - ', reward)\n",
    "    print('Loss - ', loss)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(losses)), losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(rewards)), rewards)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rewards_correct = rewards\n",
    "\n",
    "plt.plot(range(len(rewards_correct)), rewards_correct)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_avg_window = 100\n",
    "averaged_rewards = np.convolve(rewards_correct, np.ones(moving_avg_window), 'valid') / moving_avg_window\n",
    "\n",
    "plt.plot(range(len(averaged_rewards)), averaged_rewards)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 44.0\n",
      "1 29.0\n",
      "2 93.0\n",
      "3 162.0\n",
      "4 121.0\n",
      "5 28.0\n",
      "6 63.0\n",
      "7 109.0\n",
      "8 201.0\n",
      "9 351.0\n",
      "10 76.0\n",
      "11 192.0\n",
      "12 132.0\n",
      "13 66.0\n",
      "14 157.0\n",
      "15 204.0\n",
      "16 165.0\n",
      "17 88.0\n",
      "18 95.0\n",
      "19 77.0\n",
      "20 94.0\n",
      "21 309.0\n",
      "22 104.0\n",
      "23 25.0\n",
      "24 177.0\n",
      "25 154.0\n",
      "26 71.0\n",
      "27 188.0\n",
      "28 91.0\n",
      "29 68.0\n",
      "30 142.0\n",
      "31 155.0\n",
      "32 114.0\n",
      "33 175.0\n",
      "34 49.0\n",
      "35 27.0\n",
      "36 94.0\n",
      "37 175.0\n",
      "38 203.0\n",
      "39 43.0\n",
      "40 115.0\n",
      "41 173.0\n",
      "42 107.0\n",
      "43 120.0\n",
      "44 77.0\n",
      "45 61.0\n",
      "46 63.0\n",
      "47 95.0\n",
      "48 61.0\n",
      "49 119.0\n",
      "50 159.0\n",
      "51 47.0\n",
      "52 340.0\n",
      "53 420.0\n",
      "54 177.0\n",
      "55 56.0\n",
      "56 119.0\n",
      "57 50.0\n",
      "58 227.0\n",
      "59 186.0\n",
      "60 192.0\n",
      "61 193.0\n",
      "62 99.0\n",
      "63 98.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-2f757b6c317a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0meval_reward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmoving_avg_window\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0m_R\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_R\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0meval_reward\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0m_R\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/BITS/NNFL/Design Project/Double-DQN/agent.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meps_decay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m             \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m             \u001b[0mtotal_reward\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/BITS/NNFL/Design Project/Double-DQN/game.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0mscreen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menvWrapped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;31m# # DEBUG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/RL/gym/gym/wrappers/atari_preprocessing.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_skip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0mR\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgame_over\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/RL/gym/gym/wrappers/time_limit.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Cannot call env.step() before calling reset()\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mobservation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_max_episode_steps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/RL/gym/gym/envs/atari/atari_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, a)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0mnum_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp_random\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframeskip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframeskip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             \u001b[0mreward\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0male\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_obs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/hdd/anaconda3/envs/pytorch/lib/python3.7/site-packages/atari_py/ale_python_interface.py\u001b[0m in \u001b[0;36mact\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0male_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgame_over\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "eval_reward = 0\n",
    "for i in range(moving_avg_window):\n",
    "    _R = agent.evaluate()\n",
    "    print(i, _R)\n",
    "    eval_reward += _R\n",
    "eval_reward /= moving_avg_window\n",
    "\n",
    "print('\\nAvg. reward is ', eval_reward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CJZv3osMDExOyZoQV1cUMlWiqJiWmVIyE4dYM9R2AWQCuAbAGwKlqQp0gpAV7DpVizrpdEWVTVmyLf5JUTXnTY/Du7AJUMvBj/s6E1ptuly7ekc6MNd5C8tN5iVQ/uDVDLQVQCqA7gJ4AuhNRncCkEgSP/Patebj2zbmoqKx63f74/gL8+tXZCalfj34pr6jEy9PW4nCptwleQvpDDsPHHS7W1LBSkF4UgBsF+/6cAnz6U5XfK13W4AYAMPM9AEBE9QHcBOAdAMfC/8Q8QbF5dzFaNMxGdmZGqkWp1qzeth+APhIILg5l3MItePabn3HgSDkeuPikwM5Tk0hqbzlZJ3PxiPmawW34AXaK4x/jV/ioOX7cmqHuIKJPASyGloZjDLScTUIcFJeW4+ynp+P+L+INLhOCRu90lpRrI4pDpeUplCYYEm02SjeTitXvM45Egzuv+3MkQRzfuDVD1QHwHIAuKlLpUeX0FuKgpKwSAPD9z5KeJF2Jes+V1kg3e3w8BD4fIE2v1dZ9h3HCg5MiTDqJxM/PLq/Q2oR0mbVtxG001DMAsgDcAABE1JyI2gcpmCCkE+Z3N03bv7Qi6OCARZv2IGfERGzb5y+KS88yO35x+iSEKKvQrllWKOTaIZ+sIAwvWWf/BuABVZQF4IOghDraqEm91FST6EtpfmHTpcOXv+MAbv9oIcpUTzRI4g3xDWq+xdjZBQCA2evSM4orHDrr4ZjySu1+Zmaky5NWhVsz1BXQUpIfAgBm/gVAg6CEEgS/BKV4zWaBVCv4v36xFBOXbg1ksqCZb3xMRnv7xw2YuTa2eXXz7mIMfy8PRzymDjdjZbKxzD7Ksb/7Zf6G3Rjx5dKE9PDL1cgigyjtfJmuoqEAlDIzExEDgEopLiSIdLRPChrm9/9ovFd+nK6P/29l+LNdG/rY/1Zi6srtmLGmCIO6H+v5HInS1/He06vfmAMAeOrXPTB/w66o7V50SLm62GWVlZi3YXd8giUYtyOLz4joDWgLD/0BwLfQZnILQloRlMkjOgY/XWyH6RXN44UMdU0rXdQfq0GPNwOsfnovSsNu5LKuKL6kk7qDOyvkehHTtJtn8SwRXQBgP4ATAYxk5qmBSiYIPmAG9hWXYeHmxKSojgqGShOvRSKkYGaMnrke21SaD7tGx01j7oeMkPYr/IavptoUaMZWHA9y6g7uDIPPIl1Gs27NUFDKYSoAEFEGEf2WmT8MTLKjiHR76Ks7f/wgD3PXWw/h12w7gM4t6zvO1jUTNa5I8T1LxOkLdhXjqcmrHferiPNkdoeHQu5HFrGItzFNl8YYqFKcIYNQqX7WdGKOdYioIRE9QEQvE9FAtabEHdBShl+dHBEFwRv5Ow5als9ZtwsXvjATH8zd6LqumuyzqKh0F0kV2MiC4qvf61FeTZRexbIz13k5r34t0vExcxpZvA9gD4A5AH4P4K8AagEYwsyLA5ZNEFxDRAAzmO3NGht2avbkFb/s93GCyK+p7u0lszGJ12dhd3wobIaKq3oPcrjcz2/9Po+zqsNYl1MHJVnPopOy6MDMPQCAiN4CsBNAO2Y+ELhkguCTcgcbeKyXb/ziLWjTpA76HN8UgP08i7Rbq8EFq7buR5djGzia4F6ethYNamdh2Jk5AACXAxDPhB3chvt176eLUbjnMK4/43gs3bwXf7+kq+3x8SqxlVsjw47ToTev/6agRnPx4KQswiucM3MFEW0QRZF4apJpI9UwOK58P3d9og2YC0YNjijXHdv6vUqXd9mtHD+sLcINb8/HP6/ogetOaxfz2Ge/+RkAwsqiIt6RhU152MFtqH/coi0AgPkFms8plrLQMSo/t0r8ytdmI2+jvyCIkvIK6+yyNqf2cvn0XfcWl8XcLxU4KYteRKSP2QlAHfWdoC1i1zBQ6Y4S0qXhqc4Ye5l2yiIRS1amSzRULOat34UdB0pwaa/jwmW6Cc7cmzZia3OP2wwFfL18G+pnZ+KsTs3C5eQhdNayXtP3BRt3x0zdYdzfr6IAgBP//rVN/Ql4kdO4LYipLJhZ8mYL1QpmN42P9wY/aga35xqCwWpUes3ouQAQoSziUXGJyIR66wcLAESO2DJUeE2l3xOow/7y2WJc1us4/Oa1OfGIWFVtgntvntaz8NOhSdLT6H7mhyCkMXovlRFw2uk0G1gwa6nugyS4aKg451moRrIszthe82hx/c5Dvq6p28v0f4u22Cqk71bt8HzeZCHKQqhx2LU98bR5Qc+zWPnLfizzkOdJV46Tlm1D15FTsHyLw7EuUqvbOb5jNeartvqILFPo0VBOAQlu6PHIlLjr0NlbXIY/vJeXsPrMiuHuTxfjK+WbMfPhvGDSpScCURYp4v05BXG9aII1bkwIXgIK9Pr0Y4IaWFz84g+49OUfXe+vyzXjZ60nuuKX2MrCKgOq+Ur5Mb9c9J8fwp/LPcbAxuP/eWV6PiYtq0pweOCI80jAy8+blR+d4ymR7D5UGmj9QSDKIokcKql6oP8xfgWue2teCqWpmdi1B+/NKcDf/2+5t7qYwz1Ac8NWHUNng0aPogoCs1p5ZsqawM4Vi1gKNWWBKkk6ryiLJJG/4wC6PTwFn+dtTrUoRyVPTapKa+G2PztjTVFUYjivaUKCIiyHngTP5a+K1aCt3XEQh0uj04W7bQSXbN7rbkeF8VI+N/VnvDI939PxicbNrY11LfSIs6hjfMqTbqRMWaj8UouI6H/qe3simkdEa4noUyKqpcqz1fd8tT0nVTLHw5ptWgqKaavT14FVE7B7mY0Ngdv2fu/hKlOB+ZiSskqs2eZtytHm3cX49auzPB3jlqenrInpW3Dzm0fPXI97P0tNYoYXv1ubhNFC/M12rBqe/zaYkVVlZfBBDG5I5cjiLgCrDN//BeB5Zu4ELcXILar8FgB7mLkjgOfVftWONOmQ1nwS2I0zKp4tew7j8ldmYW+xpkAmLtuKC1+YGf7uhlem52PhJm+9b7fsPFiC6aaOyI1j5qOk3NvCQgvimH/g1zQXhPnm8wWFia8U/sxQ8f6+LXsPo+vIxDnw/ZISZUFEbQAMhloTg7QxdX8AX6hdxgK4XH0eor5DbR9ASbYFdH5oMp6bmpheg0zA805lJXs2cZihiM/eH59xi7Zg8ea9+O/SrRHlbhyr4fMG/NSWmhzMM38uwtrt2oiWLFzcVs9idpb/JkGvz8lRPit/JzbtKk56FPK3NmGpngIe4tjqxIw1/qwOyWpSUjWyeAHA/QD0p/sYAHuZWX/zCgG0Vp9bA9gMAGr7PrV/BEQ0nIjyiCivqCj2co5eKa2oxIvfrY2rjuqcUyjVjJm1AUNemYXZ+dpay/k7DmLfYet0CHbX10//wqrNS7dU5UaszFCN6mQBcJ+mJDvT/zxct5fit2/NwznPTDcc5+0ivjlzvaf9U0WVW8n+9/3lsyX4apE2CvrdOz8lQyzfJF1ZENElAHYw8wJjscWu7GJbVQHzaGbOZebc5s2bJ0DSxCJmKP+sVr6Bwj2HAQDnP/c9rvBo+/dz+a1ecfN9jDdvkluWFe7D2NkFUeXGs1spi3VF9orViloZ8TcJbi+J33fiqcmrnHcKiFi/zc8a318uLMQ9ny6JT6gkkYqRRT8AlxFRAYBPoJmfXoC2ZKuefqQNAD3JSyGAtgCgtjcCkF6L03ognXqi1YWQRQ9tfdEh5IyYiG37jkTsa3t9fTi43RDobHEDl778Ix6esCL83eonWMnyu3d+wuWvVClWp+dPT+6XTLy+E6mMSIs1SrDdEvAjkqw2JenKgpkfYOY2zJwDYCiAacz8WwDTAVypdhsGYLz6PEF9h9o+jYNaFDhQZGjhF93ebnXXF5t8GW4ejIMl5XjifytxpCy289fNYxZrnxMenISnv469Et13q7bjl72Hw9+tQlctz2tR9sTElZb7bth5KDlPH0f8cyRdwpDtsLq3XlqeNP95nkmneRZ/A3AvEeVD80m8rcrfBnCMKr8XwIgUyZcQqqGWSzkTl2lO5URdu3ELt+CtHzfgI4fUCpZmKNP3WGaoikrGqzPWxTgauGVsHi4zzNweM2tDTJliscdjWmu3vgLvK8wF+5QH1wbHV7OX3/3ExFXYl4ZpyGPheg3uIGDmGQBmqM/rAfS12OcIgKuSKlgA1LReRjI5qGa+Wzqco1aws3FwW5T5SZC3amvk3AovCwPZPQM7D1aF35a7TIoXDpjw+BucGv54nlPfobMe9w/qXVpXFLkcr56914intSkcRlrfrd7uvrI0IJ1GFkcFVg+bFwekFaXllbji1Vn4qaDaunJcYzTZAFXXzir3kRErk4ejGcSissMm01VQGVlLyiuwtDBxczLiWbQpKtWJ7boX6n+Mumb+XBWpmG79p/u/WOq4jxefxcqt+2OaFROl9CRFeTXiyYkr8YLD7E3jWl6JpmDXISzatBcPjluW8LrTCQbjvTkbI8ru/2Ipvl6+1eaI2CTiXQ1KWTw8fgUue3kWNu8uTkh9Yb+Pj2PNjZHt5DMXdd04Zn70cS4ONCr2VC5A5SUaKn/HQdz96SL7rAJppy5jk1IzVE3hzR80O/Pd53e23SfdnXmJZvmWfThSVoHcnKYJq7OSrRvnuet3h9Ncf2aTe8vq8jsPLJxbsURHQ+kyLVIzvQ+WxJ705/rsCXz8nM4ZePhJmr5KVj97yortmLLC2tyUqCahrFxGFtWCLSaziBPVMY4rFtPX7MBVr8+OWu3skpd+xJWvJ2blMiNONv3v11hPyLR6L0MOb6ube+VFV7hpG/R9dMd5ZsidCcgPfqsa86O1E75aBil6JJG/8Ie1O8Of/7vEfjlYJ16aFt+EYbfIyCJOPpq30XknpG1nKG7+/NEiHCwpx8HScjSsnRXsyZgdzT5e2iunNSDiId6GUx+xhKKURZxOaB9imdd2eHJS7Elxrm3oPn9HKt+l2LmhvF3cLwz5q/788SLfMhUdLPF9rBdkZJFkGNWjB7a3uBTjFgaTjM0vDGcfgd12KzPgZ3mxf99r36+Lub1KKotSn7dYF1NXFgREBC7ov8+rwrBMg+AinYkZN8EYXn+7k3LZZWoMU2nRTcc3N1kTQ0VZWMDMyBkxES95zAcVayiZjAc8kY/MXZ8sxr2fLYkKJzSTzPeWbXwWRmyVhc3++4rLsHqb9YqFG3f5dy6b5SjcU+zJZKk3AG/+sAFXGcx5ervgVxkxGPPW78LyLft8RdH0evSbGHV7w42D96eC3ejzxLeezb1BEeu6G81KyUSURQrRH4h/u8g0a3zg3Qwlmbla+C2279fSaJSWe1sqMx6WFu4NryY4adlW/LL3cESKbWaG1cqdRkVs997YKesrXpuFQS/84DmVd5VM1uVmOc7613TMsPGnGNFHQHoDYA6H9ht9ZVws6ZrRc3HJSz8m/DkM4rm2Spme0iiiNHx3k6UsxGdhgZdL73bEYNwvqFubyFco2QrtYEk5Lnt5Fs47sTnG/O5U3PbhQhzXqDaObVS7SiYgypFuxquJb71aCW/o6Ln46rZ+nuW2O5uxUX/0vyts9rJHj+7K3xE5slu0aS827y5GnmpEXSftc3viOIbAizdrsjVvkO3puKe/tl/06JBFNFiiR+leIhXTMWu0jCxqKHa3deOuQ1ETzhLJroMlvnwQPuat2VJWUYnnvlkT4djT0UcwizfvRZmKeNq6/0jEYkF7istQZjW0MGD/3sT+IYtMixK5fQHdLHjzzqwCV3UZsRtBfLNyG+7/0nnymBmr6xZEh2Dhpj0JzTp7qMTfiO9oolyUReow9k5X/lJlzy4uLcfI8ctt19qNhTEZnlXv91fPzMCZo6b5kNYdf/pgIe79bAkK97izw+s9KLdDfjd7fTRvE16clo/7Pl+CHQeOYPCLP2C6WvBFX26WUdWwmcNGX/xuLcYt2hJVr7ExtndwuxDQwMJN/leMiyWHE2YHtxmncF87RqgJm8Za07GXbKa0Ir2URTqakIOaGGpGlIUFxkt/8Ys/hD93HTkF783ZiFve9bFIiUsz1L7DZY7ZUO2IVe+OA958EPrzp7dNuw6WoNyhVz973U68bRODD0SmytixvwQrftkfTrFw3+dLwufVlYWfdNmJ6mS5ff/szF6+lUW4U2ZdwDsAACAASURBVGGnLCK/bzLN8N6xPzJleyzMGXu180fiZ5nVLxZYT4w089oM52gzSx+VV4Ec2L7vCB6Z4M5UeP5z3yf47PHjNp9YvIiysMDpPdeXr6yojE4/4Vi3w/Zej34TXn9g/5EyLCt0ngvg5uXRe6RuHyvjfsWl5ejzxLd4JIbtvaS8Ete9OQ+P/886TTYAZGdWPW725hsOm6GOlHl3rntJJJgI7H0WwZzPaWSxatuBmNuNjBwffT/N1+83r812XR8AbNpVjH9Y1OsXqw5KorMhrNl+AO9aLCxlxa5D7tdcTxYyskghbofnn+Vtdp0E0Ph4O91bfWW4G9+ej0sN6avtcJK2cE8x1ivTmVsHsL4focpuPHnZtoh9ikvLw+korByRZmpnRS/ZaX7t9x8pd/RLxMLKfNP/2RmBhS4zA4s27cHZT0/DYMMoNLC5NHH+Die5SuPspR7xGVVmR7Kct9UZ8VmkEKf3vHDPYTw1eRUOHIlWFE9NXhUVwRJZN7tWRrqZwG3DY9eO3PXJYsP5XVUVWa9NxX/5rGo5SKeXeva6nREmlFiN9+Tl2+w3OmDVy1rvw8fkloMl5bji1dnYvPswVhj8W37fXyLNZLj/iLXyfeP7+NafdpJrX3F8PedE60ir9UJqajYEvzhFCCYKURY+eeP79ZbO3ze+X4/zn/s+6gbGM3Q2vy9LNu9FzoiJWGJhc7bC2JC7fa703WLJvWxLlYksVr1z1u3CdW/Ow0vT8l2de6EPO7mOnRhBxeYXHbBOteDfZwHbxHOJwEmqX/a593kkg4NWSlO0RQQyskghrp2bMV69tTFHFx7lMX3/TkUO6ZFEdvtZ1+VNW7jVcbEax2vf1BaRKdxTFRocM9VzHFE6+rwJM+VeVimC+9Hc3//POi18XHbkOI79cW3siX9B90ITXbv+rAv2yDyLFFJc6mx/d+Lj+ZFLdlatbOa9rqiGS31fo3wbXjpabttM/YyHSyswVjn/Yonu9YH1m+PIL8bV6Nzg9tfY/ey4dIX/Q8Pp8u3wosRyRkz0fH43EU7xcsDGRHe0IsoihQx8fmbcdXwwNzJKKr7lKq2ZvHwbft4eGf3S45EplgvM6GzdFznxr/fjUzF09BwcLq3A/A3RK+2Nmrza1nxk/E1+lYVlvWlgZ4hvYMD4/mfn1B5WEAUby5+syBkhecRaBz6RiLKwIBHhcXYhjgx2TM4XdYx5YGH4/FPB7ojvB46URyxdaeaWsXlYsLFKKew+VIq563fjb18uxdVvzAknbNNHM3sPV12LWKYZr41QOjZaiVrG9H9Lt7paotOKoBVlHIFmQpqSrCzWoiwCItZIwjzqcILBOFhSjjyLNbazQlW30G0zs2prdCz+qq1aJI8eAht2cMeo1bjN60h4/gbNiZ1ODszLXp4V/uzXbzLg3zOwLU4nsdvABT+ko5IW4iU5L4woi4AwjyzCi9kzMG99dKMfC2bgro8X4crX52BfcWS4bmaG99W9nWZil5RXeE7PbWeGsuv1PPuNljzuUIwF7asj64oOYcoK/6G/RLBMaZIoRFkIfhFlEQex3jtzWgZ91237jniO+y86UBKO4T9UWh5x3syMkOd+RXklY/ehUlsH5j8nVq2EFquHbUw1cdim0bdfrL7mkhdH6G/QyCS3mkeygkREWQREiAjlFZV4c+Z6HCmrCPewD7qY6Wzm7KenIytTeyIOm/JGuUmfZH6Yyio4KqeQEWPYr7Gxj9XMjBy/PPzZmNvKT2K/tFAkKWpT97vMCOAXGVjUPJL1vsh6FkFBwKd5m/HkpFU4XFaBnm0aAfAQkmnqAWYq30SxKWWzn5e/vKIS8zfsst0+e13VNmP9ew0msI27IkdHxtFSl398XXW8zTk0f0f6tlzXvTUvJed90eXERb+IGarm0apxnaScJ+kjCyJqS0TTiWgVEa0gortUeVMimkpEa9X/JqqciOhFIsonoqVE1DvZMvvFuD6F11f0fZMTXJ9UVmFKF9KsfrbnuvcdLsM/J6223GbOSmtX96+emeHqXHZtU6nBb2JOj5LoRHFCFbEmiwrVk/bH1E3KeVJhhioH8BdmPgnA6QBuJ6KuAEYA+I6ZOwH4Tn0HgIsAdFJ/wwG8lnyRrdntkEdHT8BXLzvTs7Ywrzm8Rc1+NvcMnaJ29hwqjVrYZ812+8ykZpu22UGdM2Kip7TVbnqyF/0n/nktgjvs0pMI1ZdkjRWTriyYeSszL1SfDwBYBaA1gCEAxqrdxgK4XH0eAuA91pgLoDERtQpKPi8+hVhJ3c7u1Cz8WTO4eLul203rEuhtePRs7tj1nPL41Kiy3THmkbiZ4PPQV9YpLqzQQ3JjUWbKdDpdUjwIgmuSZVlMqYObiHIAnAJgHoCWzLwV0BQKgBZqt9YAjKupFKoyc13DiSiPiPKKivzNngWAP3+00PexRnq1aRzOke9nVu74xb9YlpuDWRje645l5TFmktXOF125MVzXiSte9bYeAuAvCEAQjlaSNbJImYObiOoD+BLA3cy8P4ad2mpD1PVh5tEARgNAbm6u7+u32sPiMbGYsaZKYT36X/sFgbxy1etzIr5X2qQ8v+fTxXj+mpM9129eMtZKEWVlSBCdIKQLNXrxIyLKgqYoPmTmcap4u25eUv91W0QhgLaGw9sAsO52pxFz1ltHGyXaZvzQV8stG/SvFm3BtyutU117SSlh5RCtJcpCENKHmmqGIm0I8TaAVcz8nGHTBADD1OdhAMYbym9UUVGnA9inm6sCkS+oigPCOF/C7M+YuCyYy7TNwzrPgiAESzwp/b2QCjNUPwA3AFhGRPoSbg8CGAXgMyK6BcAmAFepbZMAXAwgH0AxgJuSK276E2s9ayvijUz1mgpEEIRg6NSiftIc3ElXFsz8I+w78AMs9mcAtwcqlIHqGON/sVr72Sz7wRLrFBzV7xcKgmBF0CntjYjx2YQ5ZLU68+2q4JbnFAQh9RAoaWYoURYmkrWerSAIQrzIyELwhdtFUJYU7osqkzQQglA9qbEzuAVBEIzMfSDKVemJYxvWTpAk1Q8ikpGFIAhHB43qZMV1fIabPP01lBDJsqqCD6pjJJcgxPvYHs2PPZGYoQRBOEoIUlk0b5AdX+WKv1zQOSH1JBoCychC8E6yHhrh6KNpvVqB1W1erz6Rx595wjFx1a1z81ntUT+7alraH8/pAAC4s3/HhNTvlixTEk8ZWQi+WFfkbW1vIVhykrQoTXXH3Ng/fGlXXJ3bxvXxx8RQZImKhCeKnMx654BOKBg1GL/r1z4xJ3DJ8kcvjJQLEjorCNWeBy4+KdUiJIwgR63mcUHvdk1QJyvD9fHGUc/0+86N2JaojKyESG2hK7hk+9ajkoAS4cCRsohVOYNClIVPWjU6esP10o3sTHmMgybIuapmK1I8phWzj8Ks5Fo2dOfD+HXvyCVziCJHQPrHZAeVmE/HzFi4aS/OHDUt8HPLW+aThrXjC/cTEkdmmoZOxmuLTxX1akX36oNcM8Hc4IbimDtgVg6VkUvKo3XjOq7qufGMHLRtGrmvUUz9mUv2LTY/U+sNpucXvv0ZXy4oDO7cgdVcDfEy1C6rqHTeyYEOzeth8cgL4q7naCcVIcMvuFhYql62e1NKqnji8u5RZVf2ifYXVCYxDY6f29mzTSP0bNMoqrxzy/oAgD7HNwEAVHj4GcbmwOyz0Od2JLtDYD6b8fsL367FXz5fgqAQZWHAy/uwfmf8zuSsUAiN6wYXZWJFokIJ0wm71/WxId0CO2fIxWjmjA7H4J9X9Igo63Jsg6BEsiVWJJPVI//7sztElbl5N245KzHO3hB5TY5HmHDHWZhwx1lRW+4c0Alf3HoGcnM0ZeFW6TEzerVtHP5eKyNkMkOpkYUHKc34iTCL0k1J1FWiLAz4GWrHk2rAfOOTYXvvdlxD38de1P3YmNtrZyX/cSoYNdj2hQky3LO03HlkSUS47rR2mD2if2ByuKGuhVlJx6rxbNs0OorLzbtxbd92Ed9H/bqHzZ6xiccMZSYzI4TcnKZoVk/rJBnDX438+6peEd8PlpTj2SuryojIcsTjNLKI1TnwMyqxMtmZCSoYQZSFAT/KIp44bvONv2/gib7rMtIixujBuDa4V247N3ZM+dmdmvuuOx7sXrnszODMQG6Uhc5xDnbyj35/WsztA7u2xNib+7o+nxk7Jd+9dUPXWZbdvBrmdmto33b464Xen+mMEDDk5NbOO1qc107Mm/rl4InLu+Pa0zSFZlYa7ZvXw/BzqkZUh0oqUCeGkrU6txV92ze1bSMa1rFWXEY5dMb8LhfzHhwQ/nzXgE4Y87tcy/OXebG1eUCUhQE/Cvk3FvZdt5xsGOYCzjluOrWo76reYz1GarlVeE45fAZ2benpvE7ncpo1++WfzgRg77OIZ6T24rWnxNxeUm69sJQTVj3BpvXtR0D/vqoXRt+Yi1919qeIO7Woj78N6oKCUYOjHNeZoZBrs4ybjlSi7PctGtZG3/ZNI8oa2IwI3JKZEcL1px8fDnU9p3OziO0hIjx48UlY+I8LcFO/HAw4qQUA4Jt7zsEHt+jKPPr3ufnJo2/MtSxvVt+6U3fD6cdHlbVrWg8tlRWjf5eWuOeCzujfpaVlRykR/lQrRFkY0F+IXqZG3Ip2TeuiYNRg9OsY+dC9fn1v1+d75LKuEd/NszONDO7RCk1cmlVqZXi7rTeekeNqP7uekE6IyNYU5TXU+Ms/nYHGDr9XV152L2wtn8qiYe1M1HWI87d6IXVnqhVPX9kTF3U/FlkWMplj542T+eLpjADA1Ht/hUz1PJj1QlYGuR5ZuDHpZVjciFNs3qWGte2fJatIw+l/PTeqrJ2Fuax+rdjPqK7zzNdcVyJN69XCw5d2Q5a6Zp1bNsBZnZpF7BN5nLO2sDN92Snqtk3rWvw2632tSueu3+Uokx9EWRjQ792vOjWLvSPswzWJCD/cf57ltodMk7TMZpKMkP3tKK2ojKlM4mFQDF/EBYbRglHewT1aRe0bCtm/PMahtT4C6dCsXsQ+BaMGV9VF1jlvOraoH36RwuGLaps51NGvsmAADR1GUX2ObxpV9t8/n4UVphm2OlfntsVr1/dBLRf3sMLlENc8MtXRnxPzSNRcb2YohApzbKli8l1nh8NMe7VphIcGO08wtLr1Z3aMfpea1c92HLkBwJNXVEVqWfXCre6vMfAgZsSaSVY3jb7VLk5HmW+lcXQe6z7PvP885KoIrlhYKZxbxuY5HucHURYG9JGFU0MBWD+ofzi7PQZ0aRHhIHzJ8FKcb2OmqZOVgRvPOB49WkeH/umUlFe6HuYnIiZe70me2LIB6tXKwCU9W8Goy/48INp/ESKy7F2uePTCiJfq8lM0e7TxxW5g6mlmhAgVFi/C6Bv6oFz16jMz9Fh37b9Z+WbFUL5WdNQbVwZOzbF/Ue2UdnZmBuplZ+LKPm0sQ1KByOfmzv4dcWpOk6hee4VLm3Mvi1BRALjjvE4AgFG/6RlR/oezI6OVMmOMLE5q1RCntNOU0c1ntcdlvY7Dv34T22Ht9HhWdbDY1uQ7+a6zw5+dTG+/V9FX15kc6zqX9jou5vHf/eVX4c9uXq2o2dOIVDK3nXtC+PN7N/fFiS0b4A+myDJjJ9PJBDjy0irLQ60M65FuMtPBibIwwKqTFStuX3+xzY0bADw0uGt4yK9jfGDbN6uHNU8Mijpu1eOD8NiQ7uhh8/IDQElZRZSyMNpxjf6CSrZPQ6A3ADqDulmPKq432E1XPDYIL1/XO+L8XY6NjqoiovDM166tqrYbUzcMO+P48APesXn9cKN8UqvI+kJkrSw6NK8fbuB0U0G4CTK9OVmZzi1AiKpGOHpEDKvfUhVLH32c8Vy/6d0mYlT07FW9Iq5fhEyG5+Oavu3w+a1nRvmq7Brw60+PbBTLbPa7o39HjLvtzPDcAh2z0zgzROEG66Lux+LNG3Mx9Z5zwtuPlGkvRO2sDBARrjnVulHWMb435ugiAPi/2/sBACoqORyV9/YwzZ7/7b3nYMnIgRHPQZaDOXVo33YoGDUY53VpEVF+54BOILL2ARqv2AnN64ejlfw48M1l9w/qgv8MPRlLRg7EOZ2bY8o956CdKT+YsYN0i0mRXNWnTUS4d882jbF45AX4z9CTo+rRcTsKTQSiLAzosd3GZ6xg1GC8cUOf8PeXr9NGCn7vUXZmRpTzzojeyD5wUZdw2Z0DOmHUb3pGPPzXndYOp3XQHNNndWwWEXHDzFj52CBc2uu4qPQG799yGj6/9Yzw99cNvw3QIi0u6dkqPEIwvgzmUcOKRy/E2icvQr+OmhwhAkZe2g1LHxmISYYeYihEaFRXG601rZdddZ1DwF9sIsAyQmQ7QtIbU/166COVegbl+c5Np8ZsbP5xSVdc0rMVJt55drgB0UcqbZpo13K8atzqWdic9WNObNnAscdtZGBXTTmPu+3MsJnHrOQOl1k7zy+2MP1ZkREi9G4XPTIyd3AyM0Lha9ntuIa4oGtLdGpZFep5RMlhlafJKiTU+N4YfS2zR/TH7BH9w9e1kjUndsGowRhwktbJ6diiQfgZMf4OP9x7QWdseGpwzH30mnVF42ZkoXeWnr6yasRm7lgOObl11O8AgLvP10Z7xnfosl7HoWDUYFzYrSVeuvYUPHNVryj/YeO6tWJGhlm9I11b+Q+Pj0V8IQY1DL2jFiJCu6Z1LR3deoSN8SY99eseWLhxT8R+V/Zpg58KdgPQJocZTUwf/v4029DLU3OaYOXW/RjY7Vg8NXk1AO3h1+SK3Fd/+a8//Xh0b90Q784uCP+O2lkZYRNYzoiJ4WPqZ2fi1JxoZfXGDX1QXsHo36Ul+ndpiffmaHUZTSTmF0pvRBtkZyn5tN647qA8/pi62LirGAAwpFdrlJZX4opT2mDKim1afSDUUw5J83yVjBDZRl+9fO0peGVGPpqoCY0PXnwS7j6/E35vsNUe27B2TEe/NoEs0ixTJysDr1/fB72P1+5799aNcP+gE3HBSS3xwLhlyAgR5m3Q7ql++xvUzowaTcbi2r5tMbhHq4gGRe8dNqqThX2Hy3BMvVo4cKQ86lizGcRLsj0AaNUo0qdz/enHY3b+TgDW/rJjVJSWlXM708IUZ2cm1TsylZWMszo2wx9/FR0aaoXZL9i3fVPMV9ffL3pjrT8bfx14Is4/qSW6HWc/qjdzRgfv4fJ3n98Zd5/fGR/P34QHxi3DkpEDw9veuME6WsoNjevUwrayIxFl2QHNdxJlYSAzgzCo27Fod0xdzDQ4qft1bIZebRvjmSt74nCp1tsy9lqv7dsuakLSs4ZhuLm3kJURsu31/v2SrriyT1u0V6YRY/io8WUMETDykq5o3iAb55/UApkZIUy4ox8ue3mWx9mvGheazFG/Pe141M7MiOgh2pnndMVpVmbjb++HXYdKtW2hKjOGbh4Z2rcterRphOev6YULukaeP0SEK/u0BQD87ctlAIDP/qiNiM7s2CzCcZoRIjSwiKCxatCsuKRnK7w0LR9N69WKcvbrc0u++NOZKC2vROe/TwZz1WjAa7goEUX1PHXFUyszhLeH5aJLq4YY8eVS7DcpDPP1jTXZzo4vbj0DP28/iOvUfIOZP2vzbqwCNh6/vDvOO7EFutv40to0qYPe7ZqgQe1MfDhvk23UT1j+EOEDhzklRsxKOBHZBy7s1hJ/PKcD/qT8C6EQRZnr7NBvtdWAd8jJsf0jOlZtRTw0rpuFbfurlMXjQ7qhfTN3IfZeqTbKgogGAfgPgAwAbzHzqESfo2HtrCizDKD1xnWTBDPjz/07hl+2RJOVEQr7Lox2cADo0boRvlm5HYDWy2xSrxYeNERY6Q1XIsKsM0KEq09t62pf44jMSOO6tSzTmRzXuE7Eb7vilOjw0IyQNkq55tR2YWURy3wHaCHP8ww9T7twT3PM/j3nd8bwczpYKhwjWRmEc09sjmFn5oTNNTcnIMVFbTVC6JvTNGyWef+W6Ea1vhpJnnnCMejRphFu6tceL03Lx539O6J+7Uz8c9Jqx3Pl5jRFrmFkectZ7ZFXsBtX9I42dTSsnRU28Znp1aYxnlSpTCorGSMu6oJ62ZmY9+AA1+G4TpiT9XU7riEmLt0aX50ZId+p4+2U84pHL0ybzMc3uAyD90O1UBZElAHgFQAXACgE8BMRTWDmlSmQxdbOHjS3n9cRBbuK8eVC68ySuo052Wk3BpzUAt+u2l4VTRQHnVrUx9odByNGBZ8OPx0bdxc7Hnv/hSdi465DmLJiO5o3yEZ2ZgYKRg0Om+Em3Xk26mVnRCmFkM3IxAwR4d2bqmZSm5W5XxrVycLku84Ojybt6HZcI/xn6Mno36VFWF6jDG6UhZnjGtfBeIucSnYsGTkQM9cWRYxEjdevZRzpb8zoPgt97sSt55yAfic0w5BXZiXsHF54e9ipGLdwS1SItpVPK1nk5jTB6m0HknKuaqEsAPQFkM/M6wGAiD4BMARA0pVFKgmFCJf0aoUvFxbiRAsHY7EykZnTfbxyXW/c/tHCiNQLH/7+tLDD0StmW/nQU9ticM9WCUnb/sTl3ZG3cU9EXad1OCbszI9FZkYIr1/fBwdLyiMa/xn3nYtf9h5G1zjyYgWNORrMjljOzrZN62Dz7mAXwWlUN8sxJDVRZGWE8MBFXdBfOaFDIUKvto1x3onNsfdwWVJkMNK2aV3cpRzV6cLIS7ph6KntcMlLPwZ+LqoO6zYT0ZUABjHz79X3GwCcxsx3GPYZDmA4ALRr167Pxo0bUyJrMliyeS96tmkU5UMor6jEs9/8jOHndAgsid70NTtwYssGjvmOhOSzr7gMu4tLHUcoQs3jp4LdqFsrw5Oj3goiWsDMlh736qIsrgJwoUlZ9GXmP1vtn5uby3l5wcxiFARBqKnEUhbp4ZVxphCA0dvaBsAvKZJFEAThqKO6KIufAHQiovZEVAvAUAATUiyTIAjCUUO1cHAzczkR3QFgCrTQ2THMvCLFYgmCIBw1VAtlAQDMPAnApFTLIQiCcDRSXcxQgiAIQgoRZSEIgiA4IspCEARBcESUhSAIguBItZiU5xUiKgIQzxTuZgB2JkicIBD54kPkiw+RLz7SWb7jmdlyicIaqSzihYjy7GYxpgMiX3yIfPEh8sVHustnh5ihBEEQBEdEWQiCIAiOiLKwZnSqBXBA5IsPkS8+RL74SHf5LBGfhSAIguCIjCwEQRAER0RZCIIgCI6IsjBARIOIaA0R5RPRiBTJ0JaIphPRKiJaQUR3qfJHiGgLES1WfxcbjnlAybyGiC5MgowFRLRMyZGnypoS0VQiWqv+N1HlREQvKvmWElHvgGU70XCNFhPRfiK6O9XXj4jGENEOIlpuKPN8zYhomNp/LRENC1C2Z4hotTr/V0TUWJXnENFhw3V83XBMH/Vc5Cv5yep8CZTR8z0N6h23ke9Tg2wFRLRYlafkGsYNM8uf5rfJALAOQAcAtQAsAdA1BXK0AtBbfW4A4GcAXQE8AuA+i/27KlmzAbRXvyEjYBkLADQzlT0NYIT6PALAv9TniwFMBkAATgcwL8n3dBuA41N9/QCcA6A3gOV+rxmApgDWq/9N1OcmAck2EECm+vwvg2w5xv1M9cwHcIaSezKAiwK+fp7uaZDvuJV8pu3/BjAyldcw3j8ZWVTRF0A+M69n5lIAnwAYkmwhmHkrMy9Unw8AWAWgdYxDhgD4hJlLmHkDgHxovyXZDAEwVn0eC+ByQ/l7rDEXQGMiapUkmQYAWMfMsWbzJ+X6MfNMALstzu3lml0IYCoz72bmPQCmAhgUhGzM/A0zl6uvc6GtTmmLkq8hM89hrdV7z/B74sbm+tlhd08De8djyadGB1cD+DhWHUFfw3gRZVFFawCbDd8LEbuRDhwiygFwCoB5qugOZRYYo5sskBq5GcA3RLSAiIarspbMvBXQFB6AFimUT2coIl/QdLl+Ol6vWapkvRlaL1enPREtIqLviehsVdZayZNs2bzc01Rdv7MBbGfmtYaydLqGrhBlUYWVbTBlccVEVB/AlwDuZub9AF4DcAKAkwFshTasBVIjdz9m7g3gIgC3E9E5MfZNyXUlbfndywB8rorS6fo5YSdT0mUloocAlAP4UBVtBdCOmU8BcC+Aj4ioYSpkg/d7mqp7fS0iOy3pdA1dI8qiikIAbQ3f2wD4JRWCEFEWNEXxITOPAwBm3s7MFcxcCeBNVJlKki43M/+i/u8A8JWSZbtuXlL/d6RKPsVFABYy83Yla9pcPwNer1lSZVUO9EsA/FaZRaBMO7vU5wXQfACdlWxGU1UynkOv9zTp95qIMgH8GsCnBrnT5hp6QZRFFT8B6ERE7VWvdCiACckWQtk33wawipmfM5Qb7fxXANCjLiYAGEpE2UTUHkAnaE6yoOSrR0QN9M/QHKHLlRx6dM4wAOMN8t2oInxOB7BPN70ETERvLl2unwmv12wKgIFE1ESZXAaqsoRDRIMA/A3AZcxcbChvTkQZ6nMHaNdrvZLvABGdrp7hGw2/JxB83NNUvOPnA1jNzGHzUjpdQ0+k2sOeTn/QolB+hqbpH0qRDGdBG3ouBbBY/V0M4H0Ay1T5BACtDMc8pGReg4CjJ6BFkixRfyv06wTgGADfAVir/jdV5QTgFSXfMgC5SbiGdQHsAtDIUJbS6wdNcW0FUAatB3mLn2sGzX+Qr/5uClC2fGj2ff0ZfF3t+xt135cAWAjgUkM9udAa7HUAXobKEBGgjJ7vaVDvuJV8qvxdALea9k3JNYz3T9J9CIIgCI6IGUoQBEFwRJSFIAiC4IgoC0EQBMERURaCIAiCI6IsBEEQBEdEWQiCDURUQZEZbGNmKSWiW4noRovyHGM2UpfnnkFEuV5lFoSgyEy1AIKQxhxm5pPd7szMrzvvJQjVExlZCIJH1NoE/yKi+eqvoyp/hIju3BQLdgAAAgBJREFUU5/7ENESIpoD4HbDsbWJ6B21ZsEiIjpPldchok9UUrxPAdQxHDOQiOYQ0UIi+lzlDQMRjSKileqYZ5N5DYSjD1EWgmBPHZMZ6hrDtv3M3BfaLNsXLI59B8CdzHyGqfx2AGDmHtBSkowlotoA/gSgmJl7AngSQB8AIKJmAP4O4HzWkjfmAbiXiJpCS3HRTR3zRIJ+syBYImYoQbAnlhnqY8P/540biKgRgMbM/L0qeh9aYkNAS+fyEgAw82oi2ggtidw5AF5U5UuJaKna/3Roi/nM0tIFoRaAOQD2AzgC4C0imgjgf3H8TkFwRJSFIPiDbT4DWm4nuzw6sZbJtDqGoC14dG3UBqK+0BZ4GgrgDgD9Y9QtCHEhZihB8Mc1hv9zjBuYeS+AfUR0lir6rWHzTP07EXUG0A5asjtjeXcAPdX+cwH0M/hF6hJRZ+W3aMTMkwDcDW1NB0EIDBlZCII9dYhoseH718ysh89mE9E8aB2uqF4/gJsAjCGiYkSmEX8VwOtEtAzaokK/Y+YSInoNwDvK/LQYKk06MxcR0e8AfExE2aqOvwM4AGC88ncQgHsS8HsFwRbJOisIHiGiAmhpw3emWhZBSBZihhIEQRAckZGFIAiC4IiMLARBEARHRFkIgiAIjoiyEARBEBwRZSEIgiA4IspCEARBcOT/AUK77Oh7pgTmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It is observed that continuing training from checkpoint doesn't load Optimizer state\n",
    "# So, it is better to get rid of continued training stats\n",
    "\n",
    "rewards_correct2 = rewards[:1900]\n",
    "\n",
    "plt.plot(range(len(rewards_correct2)), rewards_correct2)\n",
    "plt.xlabel('Epidodes')\n",
    "plt.ylabel('Rewards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_avg_window = 100\n",
    "averaged_rewards2 = np.convolve(rewards_correct2, np.ones(moving_avg_window), 'valid') / moving_avg_window\n",
    "\n",
    "plt.plot(range(len(averaged_rewards2)), averaged_rewards2)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Averaged Rewards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}