Connect4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19c225b4",
   "metadata": {},
   "source": [
    "# Connect4 Reinforcement Learning by DQN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526abe17",
   "metadata": {},
   "source": [
    "The major source of reference for coding is INM707 Lab6 by Michael Garcia Ortiz, City, University of London."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f9835f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import copy\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from tensorflow.keras.optimizers import SGD, Adam, RMSprop\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.layers import Dropout\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from abc import ABC, abstractmethod\n",
    "from random import random, randint, sample\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "import tkinter as tk\n",
    "import torch\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c52eea79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize for random seeds/states\n",
    "seeds=[12345,42,42,42]\n",
    "def randomize(seed_rng=seeds[0], seed_np=seeds[1], seed_torch=seeds[2], seed_tf=seeds[3]):\n",
    "    tf.keras.backend.clear_session()\n",
    "    os.environ['PYTHONHASHSEED'] = '0'\n",
    "    rng = np.random.default_rng(seed_rng)\n",
    "    np.random.seed(seed_np)\n",
    "    torch.manual_seed(seed_torch)\n",
    "    tf.random.set_seed(seed_tf)\n",
    "\n",
    "randomize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45e8beef",
   "metadata": {},
   "outputs": [],
   "source": [
    "class paper_game(ABC):\n",
    "    def __init__(self, start_state):\n",
    "        self.state=start_state\n",
    "    \n",
    "    @abstractmethod\n",
    "    def transition(state, action, player):\n",
    "        #return next state\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6526f7c9",
   "metadata": {},
   "source": [
    "## 1. Connect4 basic functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "104ca979",
   "metadata": {},
   "outputs": [],
   "source": [
    "class connect4(paper_game):\n",
    "    #state: 6x7 array to represent board config, with 1 representing self-checker, -1 representing opponent-checker, 0 representing empty\n",
    "    def __init__(self, start_state=np.zeros((6,7), dtype=np.int8), Q_network=None):\n",
    "        self.state = start_state\n",
    "        self.turn_count = 0\n",
    "        self.Q = Q_network\n",
    "        #self.Q = np.zeros((19683, 9)) - need to change to Q-network somewhere\n",
    "        \n",
    "    def transition(self, state, action, player):\n",
    "        #action: a list/tuple of row and column index to indicate which cell is chosen\n",
    "        #player: 1 or -1, with 1 being self-checker, -1 being opponent-checker\n",
    "        #applicable to both tic_tac_toe and Connect-4\n",
    "        #return next state\n",
    "        new_state = copy.deepcopy(state)\n",
    "        new_state[action[0]][action[1]] = player\n",
    "        return new_state      \n",
    "\n",
    "    def win_status(self, s, action, player):  \n",
    "        #note: unlike tic-tac-toe, action is needed for input, to save computation\n",
    "        #suppose state before action is not winning, so only check lines connected with action\n",
    "        #s: state before action\n",
    "        #action: list/tuple/array, unpackable to row and column index to indicate which cell is chosen\n",
    "        #player: 1 or -1, the player taking the action\n",
    "        #----return----- \n",
    "        #win: whether player wins or not with the action\n",
    "        #lose means \"if\" it is opposite player's turn instead, hypothetical only\n",
    "        w = 3 * player  # use 3 instead of 4 because the action is not yet put into board\n",
    "        r, c = action\n",
    "        f = np.fliplr(s) #flip for finding diagonal in opposite direction, i.e. upwards\n",
    "        c_f = 6 - c      #for use in flipped states\n",
    "        lines = [s[r:r+4, c].sum()] # vertical line\n",
    "        for i in range(4):\n",
    "            lines.append(s[r,  c-i:c+4-i].sum()) #horizontal line\n",
    "            lines.append(s[r-i:r+4-i, c-i:c+4-i].diagonal().sum()) #diagonal line\n",
    "            lines.append(f[r-i:r+4-i, c_f-i:c_f+4-i].diagonal().sum()) #diagonal line in opposite direction\n",
    "        win = w in lines\n",
    "        lose = -w in lines\n",
    "        return win, lose   #lose is only hypothetical check here\n",
    "    \n",
    "    def action_list(self, state):\n",
    "        #return array of shape (n, 2), where n is no of possible actions, each represented by row and column\n",
    "        row_list = 5 - np.sum(abs(state), axis=0)\n",
    "        col_list = np.where(row_list >= 0)\n",
    "        act_list = np.dstack((row_list[col_list], col_list))[0]   #use [0] instead of squeeze which causes unexpected shape when only one action is legal\n",
    "        return act_list    #note: unlike tic-tac-toe, act_list[0] is the first action instead of row indices\n",
    "    \n",
    "    def random_move(self, state, act_list=None): #unlike tic-tac-toe, it allows input of short-listed actions\n",
    "        #return action in array of shape (2,)\n",
    "        if act_list is None:\n",
    "            act_list = self.action_list(state)\n",
    "        i = np.random.randint(len(act_list))\n",
    "        action = act_list[i]\n",
    "        return action\n",
    "\n",
    "    def basic_check(self, state, player=1, act_list=None):       \n",
    "        #exploit immediate win / then avoid immediate win by opponent; otherwise return empty move\n",
    "        #act_list: allows input of short-listed actions of player to save computation\n",
    "        #--------return--------\n",
    "        #action: action that can win, or if not available then return action that blocks opponent's win\n",
    "        #win_count: dictionary, no. of ways to win immediately by each player\n",
    "        #safe_list: actions that opponent cannot win immediately \"above\", not consider win move or other moves that lose\n",
    "        #note: safe_list is list of array while act_list is array of array in action_list function\n",
    "        if act_list is None:\n",
    "            act_list = self.action_list(state)\n",
    "        action = []\n",
    "        block = []\n",
    "        safe_list = []\n",
    "        win_count = {1: 0, -1: 0,}\n",
    "        for a in act_list:\n",
    "            win, op_win = self.win_status(state, a, player)\n",
    "            if win:\n",
    "                action = a\n",
    "                win_count[player] += 1\n",
    "            if op_win:\n",
    "                block = a\n",
    "                win_count[-player] += 1   #neagtive player means opposite player\n",
    "            #check if there is trap i.e. whether opponent can win on the same column immediately above the move\n",
    "            if a[0] == 0: \n",
    "                safe_list.append(a)  #highest row has no danger above it\n",
    "            else:\n",
    "                s = self.transition(state, a, player=player)\n",
    "                #simulate opponent move immediately above, along same column\n",
    "                op_win, _ = self.win_status(s, [a[0]-1, a[1]], -player)  #note: opposite player, so win becomes op_win\n",
    "                if not op_win:\n",
    "                    safe_list.append(a)\n",
    "    \n",
    "        if len(action)==0:  #if action is still empty\n",
    "            action = block\n",
    "        return action, win_count, safe_list  #safe list does not consider win move or block move; it is a list of array, not array of array\n",
    "    \n",
    "    def basic_move(self, state, player=1):\n",
    "        #exploit immediate win and then avoid immediate win by opponent; otherwise random\n",
    "        #return action\n",
    "        action, win_count, act_list = self.basic_check(state, player=player)\n",
    "        if len(action)==0:  \n",
    "            if len(act_list)==0:  #short list from basic check can be empty if no safe space\n",
    "                act_list = self.action_list(state)\n",
    "            action = self.random_move(state, act_list)\n",
    "        return action\n",
    "    \n",
    "    def medium_move(self, state, player=1):\n",
    "        #exploit immediate win and immeidate checkmate and avoid such by opponent; otherwise random\n",
    "        #return action\n",
    "        action, win_count, act_list = self.basic_check(state, player=player)\n",
    "        if len(action)==0:  \n",
    "            if len(act_list)==0:  #short list from basic check can be empty if no safe space\n",
    "                act_list = self.action_list(state)\n",
    "            else:\n",
    "                safe_idx = []\n",
    "                for idx, a in enumerate(act_list): #note: this act_list is safe list from basic_check\n",
    "                    safe_idx.append(idx)\n",
    "                    s1 = self.transition(state, a, player=player)\n",
    "                    op_action, win_c1, op_safe_list = self.basic_check(s1, player=-player)\n",
    "                    if win_c1[player] > 1 or op_safe_list==[] or (win_c1[player]==1 and (op_action[1:2] not in np.array(op_safe_list)[:,1]) ):  #i.e. checkmate\n",
    "                        action = a\n",
    "                        break\n",
    "                        \n",
    "                    else:\n",
    "                        for op_act in op_safe_list:\n",
    "                            s2 = self.transition(state, op_act, player=-player)\n",
    "                            a2, win_c2, safe_list2 = self.basic_check(s2, player=player)\n",
    "                            if win_c2[player]==0:\n",
    "                                if  win_c2[-player]>1 or safe_list2==[] or (win_c2[-player]==1 and (a2[1:2] not in np.array(safe_list2)[:,1]))  :  #i.e. being checkmated\n",
    "                                    safe_idx.pop()\n",
    "                                    break\n",
    "                if safe_idx != []:\n",
    "                    act_list = np.array(act_list)\n",
    "                    act_list = act_list[safe_idx]\n",
    "        if len(action)==0:\n",
    "            action = self.random_move(state, act_list)\n",
    "        return action\n",
    "  \n",
    "    def display_board(self, state):\n",
    "        print('-------------------')\n",
    "        print(state)\n",
    "        print('-------------------')\n",
    "        print(np.array([[0,1,2,3,4,5,6]]))\n",
    "            \n",
    "    def display_colour(self, state):\n",
    "        fig, ax = plt.subplots()\n",
    "        im = ax.imshow(state)\n",
    "        ax.set_xticks(np.arange(7))\n",
    "        ax.set_yticks(np.arange(6))\n",
    "        ax.set_xticklabels(np.arange(7))\n",
    "        ax.set_yticklabels(np.arange(6))\n",
    "        for i in range(6):\n",
    "            for j in range(7):\n",
    "                text = ax.text(j, i, state[i, j], ha=\"center\", va=\"center\", color=\"g\")\n",
    "        ax.set_title(\"1: computer; -1: you\")\n",
    "        fig.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def play_game(self, starter = 1, policy_function=None):\n",
    "        state = np.zeros((6,7), dtype=np.int8)\n",
    "        player = starter\n",
    "        endgame = False\n",
    "        t = 0\n",
    "        if policy_function is None:\n",
    "            policy_function = self.medium_move \n",
    "        player_dict = {1: \"computer\", -1: \"you\",}\n",
    "        #self.display_colour(state)\n",
    "        while not endgame:\n",
    "            if player == -1:\n",
    "                valid = False\n",
    "                while not valid:\n",
    "                    j = int(input('enter column: (0-6)\\n')) \n",
    "                    valid = j in self.action_list(state)[:,1]          #data validation on column input\n",
    "                    if not valid:\n",
    "                        print('Invalid column input!')\n",
    "                        if j in [0,1,2,3,4,5,6]:\n",
    "                            print('The column is full!')\n",
    "                i = (5 - np.sum(abs(state),axis=0))[j]\n",
    "            else:\n",
    "                i, j = policy_function(state)\n",
    "            player_win, _ = self.win_status(state, [i,j], player)\n",
    "            state[i,j] = player\n",
    "            self.display_colour(state)  \n",
    "            if player_win:\n",
    "                print('The winner is '+player_dict[player])\n",
    "            player *= -1\n",
    "            t += 1\n",
    "            endgame = player_win or t==42\n",
    "        if not player_win:\n",
    "            print('This is a draw.')\n",
    "\n",
    "\n",
    "    def greedy_policy(self, state, network):\n",
    "        with torch.no_grad():\n",
    "            network.eval()\n",
    "            q = np.array(network(convert_state(state))).squeeze()\n",
    "            network.train()\n",
    "        act_col_list = self.action_list(state)[:,1]\n",
    "        legal_move_q = np.array(q[act_col_list])\n",
    "        j = act_col_list[np.argmax(legal_move_q)]\n",
    "        i = (5 - np.sum(abs(state), axis=0))[j]\n",
    "        action =[i, j]\n",
    "        return action\n",
    "    \n",
    "    def step(self,action, policy=None):\n",
    "        #action: one of column numbers 0-6\n",
    "        #return-----------\n",
    "        #state: new state, numpy format\n",
    "        #reward: 0, 100 or -100\n",
    "        #endgame: boolean, end game or not\n",
    "        #in this function, the agent will play one step according to the game's state.\n",
    "        #Also, the rewards of each action would be saved in the replay memory\n",
    "        reward = 0   \n",
    "        endgame = False\n",
    "        \n",
    "        j = action\n",
    "        i = 5 - np.sum(abs(self.state[:,j]))\n",
    "        win = self.win_status(self.state,[i,j], 1)[0]\n",
    "        self.state[i,j] = 1\n",
    "        self.turn_count += 1\n",
    "        if policy is None:\n",
    "            policy = self.medium_move  #medium_move may not be compatible, but not used any way \n",
    "\n",
    "        if win:  \n",
    "            reward = 100\n",
    "            endgame = True\n",
    "        else:\n",
    "            if self.turn_count == 42:\n",
    "                endgame = True\n",
    "            else:\n",
    "                input_state = convert_state(self.state) if policy is not None else self.state\n",
    "                i,j = policy(-input_state)    #negative state for opponent\n",
    "                opponent_win = self.win_status(self.state, [i,j], -1)[0]  #-1 to indicate opponent\n",
    "                self.state[i,j] = -1\n",
    "                self.turn_count += 1\n",
    "                if opponent_win:    \n",
    "                    reward = -100\n",
    "                    endgame = True\n",
    "                if self.turn_count == 42:\n",
    "                    endgame = True\n",
    "        \n",
    "        return_state = copy.deepcopy(self.state)\n",
    "\n",
    "        return return_state, reward, endgame\n",
    "\n",
    "    def win_p100(self,network, difficulty=0, record=True, verbose=0): \n",
    "        #the agent would play 100 games with the random or medium moves\n",
    "        #and the result is the number of games that agent has won\n",
    "        #verbose: num of episodes to display; 0 means no display \n",
    "        self.Q = network\n",
    "        choices = [-1,1]*50\n",
    "        win_count = 0\n",
    "        lose_count = 0\n",
    "        draw_count = 0\n",
    "        episodes = {}   #dictionary of list\n",
    "        handcraft_agent = [self.random_move, self.basic_move, self.medium_move]\n",
    "        opponent_policy = handcraft_agent[difficulty]\n",
    "        for e in range(100):\n",
    "            state = np.zeros((6,7), dtype=np.int8)\n",
    "            current_turn = choices[-1]\n",
    "            endgame = False\n",
    "            t = 0\n",
    "            if record: episodes[e] = []\n",
    "            display = verbose > e\n",
    "            if display: print('episode ',e); self.display_colour(state)\n",
    "            \n",
    "            while not endgame:\n",
    "                if current_turn == 1:\n",
    "                    i,j = self.greedy_policy(state, network)\n",
    "                else:\n",
    "                    i,j = opponent_policy(-state)        #negative state for opponent\n",
    "                t += 1\n",
    "                player_win, _ = self.win_status(state, [i,j], current_turn)\n",
    "                state[i,j] = current_turn\n",
    "                if record: episodes[e].append(copy.deepcopy(state))\n",
    "                if display: self.display_colour(state)\n",
    "                \n",
    "                if player_win and current_turn==1:\n",
    "                    win_count += 1\n",
    "                    endgame = True\n",
    "                elif player_win and current_turn==-1:\n",
    "                    lose_count += 1\n",
    "                    endgame = True\n",
    "                elif t == 42:\n",
    "                    draw_count += 1\n",
    "                    endgame = True\n",
    "                \n",
    "                current_turn *= -1\n",
    "            \n",
    "            choices = choices[:-1]\n",
    "        return win_count, lose_count, draw_count, episodes\n",
    "\n",
    "\n",
    "    def play_DQN(self, network, starter = 1,):\n",
    "        #playing against the agent which decides each move based on a Q network\n",
    "        self.Q = network\n",
    "        state = np.zeros((6,7), dtype=np.int8)\n",
    "        current_turn = starter\n",
    "        endgame = False\n",
    "        t = 0\n",
    "        player_dict = {1: \"computer\", -1: \"you\",}\n",
    "        while not endgame:\n",
    "            if current_turn == 1:\n",
    "                i, j = self.greedy_policy(state, network)\n",
    "            else:\n",
    "                j = int(input('enter column: (0-6)\\n'))\n",
    "                i = (5 - np.sum(abs(state),axis=0))[j]           \n",
    "\n",
    "            if self.win_status(state,[i,j] ,current_turn)[0]:\n",
    "                print('The winner is '+player_dict[current_turn])\n",
    "                endgame = True\n",
    "            elif t == 42:\n",
    "                endgame = True\n",
    "                print('draw')\n",
    "                \n",
    "            state[i,j] = current_turn  \n",
    "            current_turn *= -1\n",
    "            t += 1\n",
    "            self.display_colour(state)\n",
    "\n",
    "    def reset(self):\n",
    "        #reset the games state to the initial values\n",
    "        self.state = np.zeros((6,7), dtype=np.int8)\n",
    "        self.turn_count = 0\n",
    "        return self.state\n",
    "        # self.display_colour(self.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0969b7e1",
   "metadata": {},
   "source": [
    "### Let's play Connect4 with hand-crafted agent first\n",
    "\n",
    "set True to play"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e4287de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "playHandcraft = False\n",
    "if playHandcraft:\n",
    "    test1 = connect4()\n",
    "    test1.play_game()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ecae2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if playHandcraft:\n",
    "    test2 = connect4()\n",
    "    test2.play_game(starter=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca41a97",
   "metadata": {},
   "source": [
    "### set dummy game for convenience of using functions inside game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27efc84f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dum_for_fn=connect4()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbf1724",
   "metadata": {},
   "source": [
    "## 2. Memory and state conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f6f0ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#each memory of the game consists of the current state, the action of the agent, \n",
    "#the next state after the action, and the reward of that action.\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward'))\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "def convert_state(state):\n",
    "    #converting each state to a flatten tensor\n",
    "    #state = state.flatten()   #flatten inside NN instead if needed\n",
    "    state_tensor = torch.tensor(state, device=device).unsqueeze(0)\n",
    "    return state_tensor\n",
    "    \n",
    "class ReplayMemory:\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.memory = []\n",
    "        self.position = 0\n",
    "        self.counter = 0\n",
    "        self.total = 0\n",
    "\n",
    "    def push(self, state, action, next_state, reward):\n",
    "        #saving memory\n",
    "        if len(self.memory) < self.capacity:\n",
    "            self.memory.append(None)\n",
    "            \n",
    "        state_tensor = convert_state(state)\n",
    "        \n",
    "        if next_state is None:\n",
    "            state_tensor_next = None            \n",
    "        else:\n",
    "            state_tensor_next = convert_state(next_state)\n",
    "            \n",
    "        action_tensor = torch.tensor([action], device=device).unsqueeze(0)\n",
    "        reward = torch.tensor([reward], device=device).unsqueeze(0)/100*4. # reward scaling: this approximately leads to variance of 1\n",
    "        self.memory[self.position] = Transition(state_tensor, action_tensor, state_tensor_next, reward)\n",
    "        self.position = (self.position + 1) % self.capacity\n",
    "\n",
    "    def sample(self, batch_size, prioritized=False, alpha=0.5, beta=0):\n",
    "        if not prioritized:\n",
    "            output_sample = sample(self.memory, batch_size)\n",
    "        else:\n",
    "            rewards = torch.tensor([self.memory[i].reward for i in range(len(self.memory))])\n",
    "            priority = torch.abs(rewards) + 0.01\n",
    "            pp = torch.pow(priority, alpha)\n",
    "            prob = pp/torch.sum(pp)\n",
    "            N = len(self.memory)\n",
    "            weight = torch.pow((N*prob),-beta)\n",
    "            prob_w = np.asarray(torch.mul(prob, weight)).astype('float64')\n",
    "            p = prob_w/np.sum(prob_w)\n",
    "            rnd_indices = np.random.choice(N, batch_size, p=p)\n",
    "            output_sample = [self.memory[i] for i in rnd_indices]\n",
    "        return output_sample\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0a8bd1",
   "metadata": {},
   "source": [
    "## 3. Approximate Q-matrix by neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8f2f6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q networks of the game, there are several networks in this part, but the first one is currently working\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class DQN(nn.Module):\n",
    "\n",
    "    def __init__(self, input_size, size_hidden, output_size):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.fc1 = nn.Linear(input_size, size_hidden)\n",
    "        self.bn1 = nn.BatchNorm1d(size_hidden)\n",
    "        self.fc2 = nn.Linear(size_hidden, size_hidden)   \n",
    "        self.bn2 = nn.BatchNorm1d(size_hidden)\n",
    "        self.fc3 = nn.Linear(size_hidden, size_hidden)  \n",
    "        self.bn3 = nn.BatchNorm1d(size_hidden)\n",
    "        self.fc4 = nn.Linear(size_hidden, output_size)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        h1 = F.relu(self.bn1(self.fc1(x.float())))\n",
    "        h2 = F.relu(self.bn2(self.fc2(h1)))\n",
    "        h3 = F.relu(self.bn3(self.fc3(h2)))\n",
    "        output = self.fc4(h3.view(h3.size(0), -1))\n",
    "        return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d399c23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "OBS_SIZE = 42    #size of the state\n",
    "HIDDEN_SIZE = 128    #hidden nodes of the network\n",
    "ACTION_SIZE = 7     #number of actiones and nodes in the last layer\n",
    "\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target.load_state_dict(Q_network.state_dict())     #skip if double DQN \n",
    "Q_target.eval()\n",
    "\n",
    "# Q_network = DQN_CNN().to(device)\n",
    "# Q_target = DQN_CNN().to(device)\n",
    "# Q_target.load_state_dict(Q_network.state_dict())\n",
    "# Q_target.eval()\n",
    "\n",
    "#updating Q_target network after a certain epoch\n",
    "TARGET_UPDATE = 20\n",
    "\n",
    "optimizer = optim.SGD(Q_network.parameters(), lr=0.01)\n",
    "memory = ReplayMemory(40000)    #capacity of the memory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b21c6f1",
   "metadata": {},
   "source": [
    "## 4. Epsilon-Greedy Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a77cb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "class E_Greedy_Policy():\n",
    "    \n",
    "    def __init__(self, epsilon, decay, min_epsilon, Q_network=Q_network, upgrade=True):\n",
    "        self.epsilon = epsilon\n",
    "        self.epsilon_start = epsilon\n",
    "        self.decay = decay\n",
    "        self.epsilon_min = min_epsilon\n",
    "        self.Q_network = Q_network\n",
    "        self.upgrade = upgrade\n",
    "                \n",
    "    def __call__(self, state):\n",
    "        if isinstance(state, (np.ndarray, np.generic) ):\n",
    "            state_numpy = copy.deepcopy(state)\n",
    "            state_tensor = convert_state(state)\n",
    "        else:\n",
    "            state_numpy = state.squeeze().cpu().detach().numpy()\n",
    "            state_tensor = copy.deepcopy(state)\n",
    "            \n",
    "        if self.upgrade:\n",
    "            action, win_count, act_list = dum_for_fn.basic_check(state_numpy)\n",
    "            if len(action)>0:\n",
    "                return action[0], action[1]\n",
    "        #--------------------------------------------------\n",
    "        is_greedy = random() > self.epsilon\n",
    "        \n",
    "        if is_greedy:        # we select greedy action\n",
    "            with torch.no_grad():\n",
    "                self.Q_network.eval()\n",
    "                q = np.array(self.Q_network(state_tensor)).squeeze()\n",
    "                self.Q_network.train()\n",
    "            row_list = 5 - np.sum(abs(state_tensor.detach().cpu().numpy().squeeze()), axis=0)\n",
    "            act_col_list = np.where(row_list >= 0)[0]\n",
    "            legal_move_q = np.array(q[act_col_list])\n",
    "            index_action = act_col_list[np.argmax(legal_move_q)]\n",
    "        else:\n",
    "            index_action = randint(0,6)     # we sample a random action\n",
    "        row = int(5 - torch.sum(abs(state_tensor.squeeze()[:,index_action])))\n",
    "        return row, index_action\n",
    "                \n",
    "    def update_epsilon(self):\n",
    "        self.epsilon = self.epsilon*self.decay\n",
    "        if self.epsilon < self.epsilon_min:\n",
    "            self.epsilon = self.epsilon_min\n",
    "        \n",
    "    def reset(self):\n",
    "        self.epsilon = self.epsilon_start\n",
    "\n",
    "#creating the policy, with specific factors        \n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network)\n",
    "policyB= E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a444c6",
   "metadata": {},
   "source": [
    "## 5. One step of Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e58a1539",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "GAMMA = 0.99\n",
    "config = {'double':False, 'prioritized':False, 'alpha':0.5, 'beta':0.1}  #set vanilla DQN as default\n",
    "\n",
    "\n",
    "def optimize_model(config=config, GAMMA=GAMMA, BATCH_SIZE=BATCH_SIZE, Q_network=Q_network, Q_target=Q_target, memory=memory):\n",
    "    transitions = memory.sample(BATCH_SIZE, prioritized=config['prioritized'], alpha=config['alpha'], beta=config['beta'])\n",
    "    batch = Transition(*zip(*transitions))\n",
    "    # Compute a mask of non-final states and concatenate the batch elements\n",
    "    non_final_mask = torch.tensor(tuple(map(lambda s: s != np.zeros((6,7), dtype=np.int8), batch.next_state)), device=device, dtype=torch.bool)\n",
    "    #if len(batch.next_state)==0:\n",
    "        #print('empty batch.next_state')\n",
    "    #if batch.next_state == [None]*BATCH_SIZE:\n",
    "    non_final_next_states = torch.cat([s for s in batch.next_state if s != np.zeros((6,7), dtype=np.int8)])\n",
    "    # print(non_final_next_states)\n",
    "    state_batch = torch.cat(batch.state)\n",
    "    action_batch = torch.cat(batch.action)\n",
    "    reward_batch = torch.cat(batch.reward)\n",
    "    \n",
    "    # Compute Q values using policy net\n",
    "    Q_values = Q_network(state_batch).gather(1, action_batch)\n",
    "    # Compute next Q values using Q_targets     #next state Q_values set zero if terminal (i.e. no future reward)\n",
    "    next_Q_values = torch.zeros( BATCH_SIZE, device=device)\n",
    "    next_Q_values[non_final_mask] = Q_target(non_final_next_states).max(1)[0].detach()\n",
    "    next_Q_values = next_Q_values.unsqueeze(1)\n",
    "    \n",
    "    \n",
    "    # Compute targets\n",
    "    target_Q_values = (next_Q_values * GAMMA) + reward_batch\n",
    "    # Compute MSE Loss\n",
    "    loss = F.mse_loss(Q_values, target_Q_values)\n",
    "    # Optimize the model\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    # Trick: gradient clipping\n",
    "    for param in Q_network.parameters():\n",
    "        param.grad.data.clamp_(-1, 1)\n",
    "        \n",
    "    optimizer.step()\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d86fa5",
   "metadata": {},
   "source": [
    "## 6. Training by Self-play"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a7eeb4",
   "metadata": {},
   "source": [
    "First, build helper functions for playing with the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58a117ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def self_start(i_episode):\n",
    "    i_episode = 0\n",
    "    game = connect4()\n",
    "    game.reset()\n",
    "    done = False\n",
    "    total_reward = 0\n",
    "    if i_episode % 2 == 0:\n",
    "        j = np.random.choice(7, 1, p=[0.05, 0.1, 0.15, 0.4, 0.15, 0.1, 0.05])[0]\n",
    "        game.state[5, j] = -1\n",
    "        game.turn_count += 1\n",
    "    state=game.state\n",
    "    return game, total_reward, done, state\n",
    "\n",
    "def step_memory(game, total_reward, memory, policy=policy):\n",
    "    # Get action and act in the world\n",
    "    state=copy.deepcopy(game.state)\n",
    "    state_tensor = convert_state(state)\n",
    "    row, action = policy(state_tensor)\n",
    "    next_state, reward, done = game.step(action, policy)\n",
    "    total_reward += float(reward)\n",
    "    # Observe new state\n",
    "    if done:\n",
    "        next_state = copy.deepcopy(game.reset())\n",
    "        #next_state = None    \n",
    "    # Store the transition in memory\n",
    "    memory.push(state, action, next_state, float(reward))\n",
    "    return memory, total_reward, done"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea185985",
   "metadata": {},
   "source": [
    "Now, set config and build train function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "088aef6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_episodes = 20001\n",
    "config={'double':False, 'prioritized':True, 'alpha':0.5, 'beta':0.1}\n",
    "\n",
    "\n",
    "def train(Q_network=Q_network, Q_target=Q_target, policy=policy, policyB=None, config=config, num_episodes=num_episodes, GAMMA=GAMMA, BATCH_SIZE=BATCH_SIZE, memory=memory,seeds=seeds, TARGET_UPDATE=TARGET_UPDATE, test_freq=2500):\n",
    "    start_time=time.time()\n",
    "    randomize(*seeds)\n",
    "    P=[policy, policyB]\n",
    "    Q=[Q_network, Q_target]\n",
    "    P[0].reset()\n",
    "    game = connect4()\n",
    "    game.reset()\n",
    "    rewards_history = []\n",
    "    win_percent = []\n",
    "    win = [[],[],[]]\n",
    "    episodes_all = {}    #for all games in evaluations only\n",
    "    episodes = [[],[],[]]  #for 100x3 games in one evaluation loop \n",
    "    memory_filled = False\n",
    "    if config['double']:\n",
    "        P[1].reset()\n",
    "        act=0   #optionally can change code to start randomly between 0 or 1\n",
    "    else:\n",
    "        act=0   #if not double DQN, always use act=0\n",
    "    #--------------------------------------------\n",
    "    # Warmup phase!\n",
    "    while not memory_filled:\n",
    "        i_episode=0\n",
    "        game, total_reward, done, state = self_start(i_episode)\n",
    "        while not done:\n",
    "            # Get action and act in the world\n",
    "            memory, total_reward, done = step_memory(game, total_reward, memory, P[act])\n",
    "            if done:\n",
    "                i_episode += 1\n",
    "        memory_filled = memory.capacity == len(memory)\n",
    "    print('Done with the warmup')\n",
    "    #---------------------------------------------\n",
    "    # training phase\n",
    "    for i_episode in range(num_episodes):\n",
    "        # New game at every run\n",
    "        game, total_reward, done, state = self_start(i_episode)\n",
    "        while not done:\n",
    "            # Get action and act in the world\n",
    "            memory, total_reward, done = step_memory(game, total_reward, memory, P[act])\n",
    "            # Perform one step of the optimization\n",
    "            Q[act].train()\n",
    "            Q[1-act].eval()\n",
    "            l = optimize_model(config, GAMMA, BATCH_SIZE, Q[act], Q[1-act], memory=memory)\n",
    "            if config['double']: \n",
    "                act = 1-act     #to use network alternatively\n",
    "\n",
    "        P[act].update_epsilon()\n",
    "        rewards_history.append( float(total_reward) )\n",
    "\n",
    "        # Update the target network, copying all weights and biases in DQN\n",
    "        if (not config['double']) and (i_episode % TARGET_UPDATE == 0):\n",
    "            Q[1-act].load_state_dict(Q[act].state_dict())\n",
    "    \n",
    "        if (i_episode) % test_freq == 0:\n",
    "            episodes_all[i_episode] = [None,None,None]\n",
    "            for i in range(3):\n",
    "                win[i], _, _ , episodes_all[i_episode][i] = game.win_p100(Q[act], difficulty=i)\n",
    "            win_percent.append([*win])       #if append win directly, win repeats every row as it is stored as object\n",
    "            r = rewards_history[-10:]       #just last 10 episodes\n",
    "            average_reward = sum(r)/len(r)\n",
    "            print('Training at episode {}:--------------------'.format(i_episode))\n",
    "            print('reward unscaled:',  total_reward, 'eps: ', P[act].epsilon, ' loss:', l.detach().cpu())\n",
    "            print( 'average rewards unscaled in previous 10 games:', average_reward)\n",
    "            print('300 games greedy evaluation: {} win random, {} win basic, {} win medium'.format(*win))\n",
    "    \n",
    "    x = [i*test_freq for i in range(len(win_percent))]\n",
    "    win_percent = np.array(win_percent)\n",
    "    \n",
    "    plt.plot(x, win_percent[:,0], label='random')\n",
    "    plt.plot(x, win_percent[:,1], label='basic')\n",
    "    plt.plot(x, win_percent[:,2], label='medium')\n",
    "    plt.xlabel('No. of Training Episodes')\n",
    "    plt.ylabel('Win Percentage')\n",
    "    plt.title('Win Percentage vs Difficulty')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    train_time = time.time() - start_time\n",
    "    info = {'rewards_history':rewards_history, 'win_percent':win_percent, 'episodes_all':episodes_all, 'train_time':train_time}\n",
    "    print('Training ',num_episodes,' episodes done in {:.2f} seconds'.format(train_time))\n",
    "    return Q_network, Q_target, info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af36a2d6",
   "metadata": {},
   "source": [
    "## 7.a Just small testing if it is running..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3966f82d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(1.4587)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 70 win random, 2 win basic, 5 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9655470366458386  loss: tensor(1.3440)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 64 win random, 0 win basic, 0 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  2501  episodes done in 201.44 seconds\n"
     ]
    }
   ],
   "source": [
    "#vanila DQN and not upgrade\n",
    "config={'double':False, 'prioritized':False, 'alpha':0.8, 'beta':0.1}\n",
    "num_episodes = 2501\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network, upgrade=False)\n",
    "Q_target.load_state_dict(Q_network.state_dict()) \n",
    "\n",
    "Q_network, Q_target, info = train(Q_network, Q_target, policy, config=config, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df0da1da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(0.6534)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 86 win random, 1 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9655470366458386  loss: tensor(0.9723)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 64 win random, 4 win basic, 5 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  2501  episodes done in 261.89 seconds\n"
     ]
    }
   ],
   "source": [
    "#vanila DQN and upgrade policy\n",
    "config={'double':False, 'prioritized':False, 'alpha':0.8, 'beta':0.1}\n",
    "num_episodes = 2501\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network, upgrade=True)\n",
    "Q_target.load_state_dict(Q_network.state_dict()) \n",
    "\n",
    "Q_network, Q_target, info = train(Q_network, Q_target, policy, config=config, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17ba55df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(1.9716)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 68 win random, 1 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.9775944195436634  loss: tensor(0.8135)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 70 win random, 5 win basic, 4 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  2501  episodes done in 262.08 seconds\n"
     ]
    }
   ],
   "source": [
    "#Double DQN\n",
    "config={'double':True, 'prioritized':False, 'alpha':0.8, 'beta':0.1}\n",
    "num_episodes = 2501\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network)\n",
    "policyB= E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_target)\n",
    "\n",
    "Q_network, Q_target, info = train(Q_network, Q_target, policy, policyB, config=config, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48a83810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(14.5446)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 84 win random, 1 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9655470366458386  loss: tensor(12.9986)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 74 win random, 6 win basic, 2 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  2501  episodes done in 1340.83 seconds\n"
     ]
    }
   ],
   "source": [
    "#Prioritized Experience Replay\n",
    "config={'double':False, 'prioritized':True, 'alpha':0.8, 'beta':0.1}\n",
    "num_episodes = 2501\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network)\n",
    "Q_target.load_state_dict(Q_network.state_dict()) \n",
    "\n",
    "Q_network, Q_target, info = train(Q_network, Q_target, policy, config=config, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b94f251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(12.1312)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 84 win random, 0 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.977086200052921  loss: tensor(14.5722)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 65 win random, 0 win basic, 0 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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/TiZVwwdQNXxAs/nuzrr6Ha3GTlhRW8/Ct2rZtnN3JXNuZjqjSvKa9Yoaa4FUnJ+llkfSIxT4RTrJzCgpyKakIJtpbVQyf7BxW7NeUVfU1vP6mk08/OpaGuIqmfvnZIRnEvKaBtQZHd775WgMBUkeBX6RLpSWZgwdkMvQAbl87IDmlcwNsUrm0Ooo1kPq89Xrue+l1cT3nlJakB0qmOMH18lnVEkeOZmqZJZ9o756RPYD23Y28t66LbwTmqHGLgwr6uqp2dS8knloYW6rCuaK0nyGF6mSWZpTXz0i+7GczHQOHNSPAwe1rmTetG0n79Ztibq2iOsh9b4X32djXCVzRpoxsjivzd5RB/dXJbPspsAvsp/rl5PJpGGFTBpW2Gy+u7N+y85mzydU10YXiGfermPrzsamdXMy05qeZG55YShRJXPKUeAX6aXMjOL8LIrzizlkVPNKZndn7cbtvBMuBrGH1t5Yu4lHl61lZ+PuIt5+ORmtekWNXSD6q5K5T1LgF+mDzIzBhTkMLszhY2OaL2to3MX7G7Y2ez7hndp6Fr+7nnmtKpmzmi4ITeMyh+aoqmTuvRT4RVJMRnoao0ryGVWSz7EHNV+2bWcjK9e1HkPhqTdquGPxqmbrDi3MaboIxHpFLS+JxlDIVCXzfk2BX0Sa5GSmM3ZQP8a2Ucm8eXtD03MJTQ+t1dUzf+kaPtq6s2m99DRjRFGs5VEBFaW7n1MYWpirSub9gAK/iCSkIDujzUpmgPX1O5r1ihprgfTsinVs2bG7kjk7I6pk3v18wu7nFEoLVMncXRT4RWSfFeVnUZSfxdSRRc3muzsfbtrequjo7Zp6nni9hh2Nu7u3KMjOaNbqKL6H1MJcVTJ3JQV+EUkaM2NQ/xwG9c/h8NElzZY17nJWb9jarGuLd2rreWnlBu5fupq43i0oyc9q8/mE8pJ8crNUydxZCvwi0iPS06JeTUcU53HMgWXNlm1vaGTluljLo93PKSx8s5Y7W1QyDynMaWp1FN9D6oiiPLIyVMncFgV+EdnvZGekc8DAAg4YWAAMarasfnsD1XX1cc8nRO8PvryG9VuaVzIPD5XM8b2iVpTmM3RALukpXMmswC8ivUp+dgYThxYycWjrSuYNW3Y0dX7XNC5zXT3Pr1hHfVwlc1ZGGqOK81oVHVWU5lPWL7vPVzIr8ItInzEgL4spI7OY0kYlc018JXNcR3hPvlHDjobdlcz5WelNzye0HJd5QF5Wd2cpKRT4RaTPMzMG9s9hYP8cDmunkrm6LlQwhyapL7//EQ++8gGNcbXMRXmZLXpFLQhNU/PJy+o94bT3pFREJAniK5mPGtu8knlHwy5Wrt/S7PmE6tAJ3t0vvN9s3UH9s1s9tFZRGu03O2P/anmkwC8i0o6sjDTGlBUwpqyg1bKtOxqbfiXEvx5+9QPq6nc0rZdmMLwor+lXQnlJHhVlBYzuwUpmBX4Rkb2Qm5XO+CH9GT+kf6tlH23d2ezZhNj0ne+uZ/P23WMoZKWnMbIkr9WzCaPL8hmYxEpmBX4RkS5WmJvJ5BEDmDxiQLP57k7t5h3NekWNjaPwVItK5rysdMpL8pl94kQOrSimKynwi4h0EzOjrF82Zf2yWwXzXbucNRu3hdZGu59P6JfT9WFagV9EZD+QlmYMG5DLsAG5HDm2NLnHSureRURkv6PALyKSYhT4RURSjAK/iEiKUeAXEUkxCvwiIilGgV9EJMUkNfCb2QAzu9PMXjezZWZ2hJkVm9kjZvZmeC/a855ERKSrJPuO/2rgIXcfB0wGlgGXAo+5+1jgsfBZRES6SdICv5n1B44GrgNw9x3uvgE4CbgprHYTcHKy0iAiIq0l845/NFAD3GBmS8zsj2aWDwxy9zUA4X1gWxub2blmtsjMFtXU1CQxmSIiqSWZgT8DmAr8zt2nAPV0oljH3ee4+zR3n1ZWVrbnDUREJCHJDPyrgFXu/mz4fCfRhWCtmQ0BCO8fJjENIiLSQtICv7t/AKw0s4PCrJnAa8A84Mww70zgvmSlQUREWkt2t8z/Dsw1syzgHeDrRBeb283sG8B7wJeSnAYREYmzx8Bv0dhfpwOj3f3HZjYSGOzuz+1pW3d/EZjWxqKZnU2oiIh0jUSKeq4FjgC+Ej5vAn6btBSJiEhSJVLUc5i7TzWzJQDuvj4U3YiISC+UyB3/TjNLBxzAzMqAXR1vIiIi+6tEAv81wD3AQDP7GbAQ+HlSUyUiIkmzx6Ied59rZouJKmQNONndlyU9ZSIikhSJtOopJnrI6ta4eZnuvjOZCRMRkeRIpKjnBaI+d94A3gzTK8zsBTM7JJmJExGRrpdI4H8IOM7dS929BPgccDvwbaKmniIi0oskEvinufvfYh/c/WHgaHf/J5CdtJSJiEhSJNKOf52ZfR/4S/h8KrA+NPFUs04RkV4mkTv+rwLDgXuJOlQbGealA19OWspERCQpEmnOWUvU2Vpb3ura5IiISLIl0pyzDPgeMBHIic13908kMV0iksJ27tzJqlWr2LZtW08npVfIyclh+PDhZGZmJrR+ImX8c4HbgOOBbxH1oa+xEEUkaVatWkW/fv0oLy8n6iBY2uPu1NXVsWrVKioqKhLaJpEy/hJ3vw7Y6e5PufvZwOH7klARkY5s27aNkpISBf0EmBklJSWd+nWUyB1/7AndNWb2eWA1UWWviEjSKOgnrrPnKpHA/1MzKwQuBn4N9Acu7HTKRERkv5BIUc96d//I3V9x94+7+yHAumQnTESkLykvL6e2trankwEkFvh/neA8EZE+yd3ZtavvPK/abuA3syPM7GKgzMy+G/eaTfTwlohIn1VdXc348eP59re/zdSpU/nGN77BtGnTmDhxIrNmzWpar7y8nFmzZjF16lQqKyt5/fXXAairq+PTn/40U6ZM4V//9V9x96ZtrrrqKiZNmsSkSZP41a9+1XS8cePGcc455zBp0iROP/10Hn30UWbMmMHYsWN57rk9DnOesI7K+LOAgrBOv7j5G4EvdlkKREQ68KO/vsprqzd26T4nDO3PrBMm7nG95cuXc8MNN3Dttdeybt06iouLaWxsZObMmSxdupSqqioASktLeeGFF7j22mu58sor+eMf/8iPfvQjjjzySC677DLuv/9+5syZA8DixYu54YYbePbZZ3F3DjvsMI455hiKiop46623uOOOO5gzZw7Tp0/nlltuYeHChcybN4+f//zn3HvvvV2S/3YDv7s/BTxlZje6+7tdcjQRkV5k1KhRHH541Hr99ttvZ86cOTQ0NLBmzRpee+21psD/hS98AYBDDjmEu+++G4AFCxY0TX/+85+nqKgIgIULF3LKKaeQn5/ftO3TTz/NiSeeSEVFBZWVlQBMnDiRmTNnYmZUVlZSXV3dZflKpFVPtpnNAcrj19eTuyLSHRK5M0+WWHBesWIFV155Jc8//zxFRUWcddZZzdrNZ2dHHRWnp6fT0NDQNL+tZpbxRT4txfYDkJaW1vQ5LS2t2X73VSKVu3cAS4AfAv8R9xIRSQkbN24kPz+fwsJC1q5dy4MPPrjHbY4++mjmzp0LwIMPPsj69eub5t97771s2bKF+vp67rnnHo466qikpr+lRO74G9z9d0lPiYjIfmry5MlMmTKFiRMnMnr0aGbMmLHHbWbNmsVXvvIVpk6dyjHHHMPIkSMBmDp1KmeddRaHHnooAOeccw5Tpkzp0qKcPbGOfnYAhFY8HwL3ANtj892929ryT5s2zRctWtRdhxORHrZs2TLGjx/f08noVdo6Z2a22N2ntVw3kTv+M8N7fPGOA6P3OoUiItJjEumPP7Hu3kREpFfYY+WumeWZ2Q9Dyx7MbKyZHZ/8pImISDIk0qrnBmAH8LHweRXw06SlSEREkiqRwD/G3a8gdM/s7lsB9ZcqItJLJRL4d5hZLlGFLmY2hrjWPSIi0rskEvhnAQ8BI8xsLvAY0Ri8IiJ9VnV1NZMmTdqnfcybN4/LL7+8i1LUdRJp1fOImb1ANNyiAd9x9/2jU2kRkf3YiSeeyIknntjTyWglkVY9pxA9vXu/u88HGszs5KSnTESkhzU0NHDmmWdSVVXFF7/4RbZs2cKPf/xjpk+fzqRJkzj33HOb+t655pprmDBhAlVVVZx22mkA3HjjjZx//vkArF27llNOOYXJkyczefJk/vGPf/RYvhJ5gGuWu98T++DuG8xsFnBv0lIlIhLz4KXwwctdu8/BlfC5PRfBLF++nOuuu44ZM2Zw9tlnc+2113L++edz2WWXAfC1r32N+fPnc8IJJ3D55ZezYsUKsrOz2bBhQ6t9XXDBBRxzzDHcc889NDY2snnz5q7NUyckUsbf1jqJXDBERHq1ESNGNPXLc8YZZ7Bw4UKeeOIJDjvsMCorK3n88cd59dVXAaiqquL000/n5ptvJiOjdYh8/PHHOe+884CoF8/CwsLuy0gLiQTwRWZ2FfBbopY9/w4sTmqqRERiErgzT5aW3SqbGd/+9rdZtGgRI0aMYPbs2U3dM99///0sWLCAefPm8ZOf/KTpgrA/SuSO/9+JHuC6Dbgd2Ar8W6IHMLN0M1tiZvPD52Ize8TM3gzvRXuTcBGRZHvvvfd45plnALj11ls58sgjgWjErc2bN3PnnXcCsGvXLlauXMnHP/5xrrjiCjZs2NCqKGfmzJn87ndRR8eNjY1s3Ni1o4p1RoeB38zSgfvc/VJ3nxZeP3D3+k4c4zvAsrjPlwKPuftYoqahl3Y61SIi3WD8+PHcdNNNVFVVsW7dOs477zy++c1vUllZycknn8z06dOBKJCfccYZVFZWMmXKFC666CIGDBjQbF9XX301TzzxBJWVlRxyyCE9+osgkW6Z5wFfc/ePOr1zs+HATcDPgO+6+/Fmthw41t3XmNkQ4El3P6ij/ahbZpHUom6ZO6+ru2XeBrxsZo8ATXf67n5BAtv+iuhhr/jB2ge5+5qwjzVmNrCtDc3sXOBcoGkAAxER2XeJBP77w6tTQg+eH7r7YjM7trPbu/scYA5Ed/yd3V5ERNqWyJO7N4W+eka6+/JO7HsGcKKZHQfkAP3N7GZgrZkNiSvq+XCvUi4iInslkSd3TwBeJOqvBzM7OJT7d8jd/9Pdh7t7OXAa8Li7nwHMY/eoXmcC9+1d0kVEZG8k0pxzNnAosAHA3V8E9mVUrsuBT5nZm8CnwmcREekmiZTxN7j7Ry0eZOhUmbu7Pwk8GabrgJmd2V5ERLpOInf8r5jZV4H0MOzir4Ge611IRKSXOfbYY4k1ST/uuOPa7MunOyX65O5EosFXbgE+Ai5MYppERPqsBx54oNXDXd2t3cBvZjlmdiFwBfAecIS7T3f3H7r7tu5KoIhIT6iurmbcuHGcc845TJo0idNPP51HH32UGTNmMHbsWJ577jnq6+s5++yzmT59OlOmTOG++6K2Klu3buW0006jqqqKU089la1btzbtt7y8nNra2lYDvVx55ZXMnj0biH4hXHTRRRx99NGMHz+e559/ni984QuMHTuWH/7wh/uct47K+G8iGmf3aeBzwHh0py8i3ewXz/2C19e93qX7HFc8ju8f+v09rvfWW29xxx13MGfOHKZPn84tt9zCwoULmTdvHj//+c+ZMGECn/jEJ7j++uvZsGEDhx56KJ/85Cf5wx/+QF5eHkuXLmXp0qVMnTq102nMyspiwYIFXH311Zx00kksXryY4uJixowZw0UXXURJScneZB3oOPBPcPdKADO7Dnhur48iItILVVRUUFlZCcDEiROZOXMmZkZlZSXV1dWsWrWKefPmceWVVwKwbds23nvvPRYsWMAFF0SdG1RVVVFVVdXpY8dG7qqsrGTixIkMGTIEgNGjR7Ny5cqkBf6dsQl3b2jZPamISHdI5M48WbKzs5um09LSmj6npaXR0NBAeno6d911Fwcd1Lq7sT3FzIyMDHbt2tX0Oda9c8tjxx83/tj7oqPK3clmtjG8NgFVsWkz67n+REVE9hOf+cxn+PWvf900/OKSJUsAOProo5k7dy4Ar7zyCkuXLm217aBBg/jwww+pq6tj+/btzJ8/v9vS3e4dv7und1sqRER6of/+7//mwgsvpKqqCnenvLyc+fPnc9555/H1r3+dqqoqDj74YA499NBW22ZmZnLZZZdx2GGHUVFRwbhx47ot3Xvslnl/oG6ZRVKLumXuvM50y5xIO34REelDFPhFRFKMAr+I7Jd6QzH0/qKz5yqRbpm/EAZG/0itekSkO+Tk5FBXV6fgnwB3p66ujpycnIS3SaR3ziuAE9x92R7XFBHpAsOHD2fVqlXU1NT0dFJ6hZycHIYPH57w+okE/rUK+iLSnTIzM6mo2JdhP6QjiQT+RWZ2G3AvUQ+dALj73clKlIiIJE8igb8/sAX4dNw8BxT4RUR6oUQGW/96dyRERES6R7uB38y+5+5XhBG3WlWtu/sFSU2ZiIgkRUd3/LEKXfWVICLSh3QU+MeY2XRgrrvvWx+gIiKy3+go8A8HrgbGmdlSogHW/w484+7ruiNxIiLS9TrqlvkSADPLAqYBHwPOBv7PzDa4+4TuSaKIiHSlRJpz5hI16SwMr9XAy8lMlIiIJE9HrXrmABOBTcCzREU9V7n7+m5Km4iIJEFHnbSNBLKBD4D3gVXAhm5Ik4iIJFFHZfyftWi04IlE5fsXA5PMbB1RBe+sbkqjiIh0oQ7L+D3qE/UVM9sAfBRexwOHAgr8IiK9UEdl/BcQ3enPAHYSmnIC16PKXRGRXqujO/5y4E7gIndf0z3JERGRZOuojP+73ZkQERHpHhpzV0QkxSjwi4ikGAV+EZEUo8AvIpJiFPhFRFKMAr+ISIpR4BcRSTFJC/xmNsLMnjCzZWb2qpl9J8wvNrNHzOzN8F6UrDSIiEhrybzjbwAudvfxwOHAv5nZBOBS4DF3Hws8Fj6LiEg3SVrgd/c17v5CmN5ENHj7MOAk4Kaw2k3AyclKg4iItNYtZfxmVg5MIRrQZVCs75/wPrCdbc41s0VmtqimpqY7kikikhKSHvjNrAC4C7jQ3Tcmup27z3H3ae4+raysLHkJFBFJMUkN/GaWSRT057r73WH2WjMbEpYPAT5MZhpERKS5ZLbqMeA6YJm7XxW3aB5wZpg+E7gvWWkQEZHWOhyBax/NAL4GvGxmL4Z5PwAuB243s28A7wFfSmIaRESkhaQFfndfCFg7i2cm67giItIxPbkrIpJiFPhFRFKMAr+ISIpR4BcRSTEK/CIiKUaBX0QkxSjwi4ikGAV+EZEUo8AvIpJiFPhFRFKMAr+ISIpR4BcRSTEK/CIiKUaBX0QkxSjwi4ikGAV+EZEUo8AvIpJiFPhFRFKMAr+ISIpR4BcRSTEK/CIiKUaBX0QkxSjwi4ikGAV+EZEUo8AvIpJiFPhFRFKMAr+ISIpR4BcRSTEK/CIiKUaBX0QkxSjwi4ikGAV+EZEUo8AvIpJiFPhFRFKMAr+ISIpR4BcRSTEK/CIiKUaBX0QkxfRI4Dezz5rZcjN7y8wu7Yk0iIikqm4P/GaWDvwW+BwwAfiKmU3o7nSIiKSqjB445qHAW+7+DoCZ/QU4CXitqw/0i9tP4PUta7p6tyIi3WZc3hC+/+W/duk+e6KoZxiwMu7zqjCvGTM718wWmdmimpqabkuciEhf1xN3/NbGPG81w30OMAdg2rRprZYnoquvkiIifUFP3PGvAkbEfR4OrO6BdIiIpKSeCPzPA2PNrMLMsoDTgHk9kA4RkZTU7UU97t5gZucDfwPSgevd/dXuToeISKrqiTJ+3P0B4IGeOLaISKrTk7siIilGgV9EJMUo8IuIpBgFfhGRFGPue/VsVLcysxrg3b3cvBSo7cLk9AbKc2pQnlPDvuR5lLuXtZzZKwL/vjCzRe4+rafT0Z2U59SgPKeGZORZRT0iIilGgV9EJMWkQuCf09MJ6AHKc2pQnlNDl+e5z5fxi4hIc6lwxy8iInEU+EVEUkyfDvx9dVB3M6s2s5fN7EUzWxTmFZvZI2b2Zngvilv/P8M5WG5mn+m5lCfOzK43sw/N7JW4eZ3Oo5kdEs7VW2Z2jZm1NRDQfqGdPM82s/fDd/2imR0Xt6wv5HmEmT1hZsvM7FUz+06Y32e/6w7y3H3ftbv3yRdRl89vA6OBLOAlYEJPp6uL8lYNlLaYdwVwaZi+FPhFmJ4Q8p4NVIRzkt7TeUggj0cDU4FX9iWPwHPAEUQjvz0IfK6n89bJPM8GLmlj3b6S5yHA1DDdD3gj5K3Pftcd5Lnbvuu+fMffNKi7u+8AYoO691UnATeF6ZuAk+Pm/8Xdt7v7CuAtonOzX3P3BcC6FrM7lUczGwL0d/dnPPov+VPcNvuddvLcnr6S5zXu/kKY3gQsIxqDu89+1x3kuT1dnue+HPgTGtS9l3LgYTNbbGbnhnmD3H0NRH9YwMAwvy+dh87mcViYbjm/tznfzJaGoqBYkUefy7OZlQNTgGdJke+6RZ6hm77rvhz4ExrUvZea4e5Tgc8B/2ZmR3ewbl8+DzHt5bEv5P13wBjgYGAN8Mswv0/l2cwKgLuAC919Y0ertjGvV+a7jTx323fdlwN/nx3U3d1Xh/cPgXuIim7Whp9+hPcPw+p96Tx0No+rwnTL+b2Gu69190Z33wX8H7uL6fpMns0skygAznX3u8PsPv1dt5Xn7vyu+3Lg75ODuptZvpn1i00DnwZeIcrbmWG1M4H7wvQ84DQzyzazCmAsUYVQb9SpPIYigk1mdnho7fAvcdv0CrHgF5xC9F1DH8lzSON1wDJ3vypuUZ/9rtvLc7d+1z1dw53k2vPjiGrM3wb+q6fT00V5Gk1Uw/8S8GosX0AJ8BjwZngvjtvmv8I5WM5+2tKhjXzeSvRzdyfRnc039iaPwLTwD/Q28BvC0+r746udPP8ZeBlYGgLAkD6W5yOJiieWAi+G13F9+bvuIM/d9l2rywYRkRTTl4t6RESkDQr8IiIpRoFfRCTFKPCLiKQYBX4RkRSjwC9JYWZuZr+M+3yJmc1OwnFuDY+4XxQ377/iejhsjJu+IMF9/tHMJuxhnW+Z2b/sa/rDvp4MvS7G0nnnHtafZmbXdMFxbzSzL+7rfqT3UXNOSQoz20bUJn26u9ea2SVAgbvP7sJjDAaedfdRHayz2d0LWswzor/9XV2Vln1hZk8S9cq4qJuPeyMw3907vNBI36M7fkmWBqKxQi9qucDMRpnZY+FO/TEzG9nRjswsx8xuCP2OLzGzj4dFDwMDw13yUXvYR3no//xa4AVghJn9zswWhT7RfxS37pNmNi1Mbzazn5nZS2b2TzMbFObPDhez2Pq/MLPnzOyNWFrMLM/Mbg/5vM3Mno3tNxHhjvz3ZvZ02O/xYf6xZjY/TB8T90thiZn1s8j/mNkr4ZydGtY1M/uNmb1mZvezu+OzWL/uT1nU8d/fbHd3CReE9Zea2V8STbvs53r6KTa9+uYL2Az0Jxo7oBC4BJgdlv0VODNMnw3cu4d9XQzcEKbHAe8BOUA5cX3Xt5eO8F4O7AIOj1tWHN7TgSeBqvD5SWBamHbghDB9BfDDMD2b0Hd6WP+XYfo44NEwfQnwhzA9iehiOK2NND5J9ETmi+H1P2H+jcBDRDdoY4me5s0BjiW6U4+dyxlhugDIAP4f8EjI16BwvoYAX4ibPxTYAHwRyAT+AZSF/ZwKXB+mVwPZYXpAT/9d6dU1rwxEksTdN5rZn4ALgK1xi44gCkIQPaZ+xR52dSTw67DP183sXeBAoKNeHNvyrrv/M+7zly3q1jqDKDBOIHpcPt4OYH6YXgx8qp193x23Tnlcuq8O6X7FzFruO97p3nZRz+0eFUm9aWbvEF344v0duMrM5gJ3u/sqMzsSuNXdG4k6O3sKmE400Ets/mozezzs4yCiC9MjUSkY6UTFdBCdj7lmdi9wbwfpl15ERT2SbL8i6nMmv4N19lTR1FVD6NU37TDq7OoSYKa7VwH3E91Nt7TTw+0u0Ajt3ixtb2Odrkh3y3PT7LO7Xw6cA+QC/zSzcXs4blvn2oBX3f3g8Kp090+HZZ8HfgscAiw2M90s9gEK/JJU7r4OuJ0o+Mf8g6i3VIDTgYV72M2CsB5mdiAwkqhoZF/0J7oQfBTK7T+3j/try0LgywChlVDlXuzjS2aWZmZjiDroa5ZvMxvj7i+7+y+ARUS/CBYAp5pZupmVEd3pPxfmnxbmDwFidSXLgTIzOyLsM9PMJppZGjDC3Z8AvgcMICpOkl5OV2/pDr8Ezo/7fAFwvZn9B1ADfB2iJpIA7v77FttfC/zezF4mKic/y9232z6Mpe3uL5nZEqIeTt8hKjLpatcCN4UiniVExSYftbPuXDOLFYfVuvsnw/Ry4Cmisvpvufu2Fvm+MFR2NwKvEY27uoOoOO0lojv877n7B2Z2D/AJoh4g3wj7xd13hGad15hZIVFc+FVY5+Ywz4D/dfcN+3A+ZD+h5pwiSWJm6UBmCNZjiLoXPtCjMaAT2f5G1NxSkkB3/CLJkwc8YdFoSwacl2jQF0km3fGLiKQYVe6KiKQYBX4RkRSjwC8ikmIU+EVEUowCv4hIivn/OPAE0W1p8jgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  2501  episodes done in 1349.65 seconds\n"
     ]
    }
   ],
   "source": [
    "#Double DQN & Prioritized\n",
    "config={'double':True, 'prioritized':True, 'alpha':0.8, 'beta':0.1}\n",
    "num_episodes = 2501\n",
    "Q_network = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "Q_target = DQN(OBS_SIZE, HIDDEN_SIZE, ACTION_SIZE).to(device)\n",
    "policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network)\n",
    "policyB= E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_target)\n",
    "\n",
    "Q_network, Q_target, info = train(Q_network, Q_target, policy, policyB, config=config, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc242ff1",
   "metadata": {},
   "source": [
    "## 7.b Visualize evaluation episodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335ba985",
   "metadata": {},
   "source": [
    "1: computer\n",
    "\n",
    "-1: your evaluation agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a7b16015",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEYCAYAAADmlsvOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAV1klEQVR4nO3df2zd9X3v8ecrthMzey4KhK1OnNCQBqVUC3CdiKpZL7dbG2BNu38G/cGmMXWRpi5rtU29pbfSuqqVLle6FeJuu1oGrO0KmK4tiJaMtFuHOlDXYAppIdDOIzHG2ZqUBhKbBjsn7/vH+cY5xz2Jj7/2N9/v5/J6SFHOj+/3+3n5K/zK5/s5h3MUEZiZpWBJ2QHMzNrlwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS6skkn6Q0nDkl6V9Lmy8yw2SZ+T9OlzPOZSSV+WdEBSSLr6XI5vxXFhle8g8GngzrKDVJGkzpy7PgLcCPznIsaxkrmwShYRX42I+4EX8+wv6fclPSPpmKR9kq7MHt8g6WFJL0l6WtK7G/b5nKS/kvQPkiYkPSrplyXdKumIpGclXdGw/QFJN2fHPyLpbyV1Z8/9rqRHZmUKSeskbQc+AHw0G+dr2fP9kr4i6bCk/ZL+qGHfT2azoy9KOgr8rqQtkl6axzmdiohbI+IRoDbP8/lbkh6f9difSLo/u/06SV/Iso9K+oSkJQ3Zv9iw38XZuchbujaLC6vissLZcobnfgv4JPA7QB/wbuBFSV3A14BvABcBO4C7JF3asPv1wCeAC4FXge8A38vufxn47KzhPgBsBS4B1mf7nlVE7ATuAv5XRPRGxLbsl/trwF5gJfBrwEckbW3Y9T1ZhvOBuyLikYg4f67x2iXp/ZK+f4anHwDeIGlDw2M3An+X3f4/wOuAtcB/pX7ub1qsbHZ2LqyKi4jzs5lCKx+kXgaPRd1IRIwCVwG9wP/MZhvfAr4OvK9h3/si4vGIOA7cBxyPiC9ERA24F7hi1lh/ERFjEfFT4DOzjjUfm4AVEfGpLNtzwN8A723Y5jsRcX9EnIyIn+Uc54wi4u6I+JUzPPcq9Z//RgBJlwEXA1+X1AHcANwcEcci4gDwv4HfXuyM1poLK20DwL+3eLwfGIuIkw2PjVKf0Zzy44bbP2txv3fWMcdmHat/3mnr1gD92czxpexS7+PAL51hrLOStDq73JyQNJEz02yfB94vSdTL6EtZkV0ILKX+858y+7xagXxtnbYx6pdosx0EBiQtaSit1cCPFjDWQMPt1dkYAJPAL5x6QtIvz9pv9seBjAH7I+KNZxmr7Y8QiYjn+flyXZCI+FdJU8CvAu/P/gD8BJimXrr7ssdWA+PZ7aZzAcw+F7ZAnmGVTFJntoDdAXRI6p7HIu3twJ9K+i+qWydpDfBd6r88H5XUlb2svw0YWkDUD0laJWk59RnRvdnje4HLJF2e/RyfnLXfj6mv95yyBzgq6b9LOk9Sh6Q3S9p0poElXS1pXp+DJGnZqRcGgKXZedU8DvEF4C+AE6cuybPL5S8Bn5H0i9m5/mPg1EL7k8Dbslnf64Cb55PZ5ubCKt8nqF+CfYz6usnPaFjQzi51frXVjhHx99TXk+4GjgH3A8sjYor6Avy11GcFfwX8TkQ8u4Ccd1NfxH8u+/PpLMOPgE8B/wj8G/W3EzS6A3hTdvl3f/ZLvw24HNif5bud+kL2mQxQf1FgPn5I/VyuBHZnt9cASPqApKfn2P/vgDdzerH9lB3U/zF4jvrPejfZW1Ii4pvUi/z7wOPU1w1tEckf4GdzkXQA+GBE/GNJ498O/H1E7D6HY54HHAKujIh/O1fj2tl5DcsqLyI+WMKwfwA85rKqFheW2SzZjFLAb5abxGbzJaGZJcOL7maWjEIuCS9c3hEXD3QVcWgz+//cgbFpfvLTWsu3oBRSWBcPdLFn98DcG5qZzbJ565n/RwdfEppZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpaMyhbWQ89PsmFolPX3jHLLE0fKjtPE2fJxtnyc7bS2CkvSNZJ+KGlE0seKDlU7Gex49DAPXtfPU9evZmjkGPuOTBU9bFucLR9ny8fZms1ZWJI6gL+k/h13bwLeJ+lNRYbac+g4l/R1sbavi6Ud4oZ1vTxwYLG+hXxhnC0fZ8vH2Zq1M8PaDIxExHPZF3QOAe8pMtT4KzUGek9/xPLKnk7GJ2tFDtk2Z8vH2fJxtmbtFNZKoPEzS1/IHmsiabukYUnDh19cWOhWX+Qzn+8YL5Kz5eNs+Thbs3YKq1WGn4saETsjYjAiBldc0LGgUKt6OhibmJ65Pz55gv6ehR1zsThbPs6Wj7M1a6ewXgAav1FiFXCwmDh1my7qZuTlafYfnWaqFtw7MsG2NT1FDtk2Z8vH2fJxtmbtfGvOY8AbJb0BGAfeC7y/0FBLxG1bVnDtroPUIrjp0j4uW76syCHb5mz5OFs+ztasrW9+lnQdcCvQAdwZEZ852/aDG7vDX/NlZnls3jrG8N7j+b+XMCJ2AbsWNZWZ2TxV9p3uZmazubDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZFS2sB56fpINQ6Osv2eUW544UnacJs6Wj7Pl42ynzVlYku6UdEjSU4WnydROBjsePcyD1/Xz1PWrGRo5xr4jU+dq+LNytnycLR9na9bODOtzwDWFpphlz6HjXNLXxdq+LpZ2iBvW9fLAgYlzGeGMnC0fZ8vH2ZrNWVgR8W3gp4WmmGX8lRoDvV0z91f2dDI+WTuXEc7I2fJxtnycrdmirWFJ2i5pWNLw4RcXFjqixfEXdMTF42z5OFs+ztZs0QorInZGxGBEDK64oGNBx1rV08HYxPTM/fHJE/T3LOyYi8XZ8nG2fJytWSVfJdx0UTcjL0+z/+g0U7Xg3pEJtq3pKTsW4Gx5OVs+ztass9Cj59S5RNy2ZQXX7jpILYKbLu3jsuXLyo4FOFtezpaPszVTtLoQbdxAuge4GrgQ+DHwZxFxx9n2GdzYHXt2DyxWRjN7Ddm8dYzhvcdbLofNOcOKiPctfiQzs/mr5BqWmVkrLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2RUtrAeen6SDUOjrL9nlFueOFJ2nCbOlo+z5eNsp81ZWJIGJP2zpGckPS3pw0WHqp0Mdjx6mAev6+ep61czNHKMfUemih62Lc6Wj7Pl42zN2plhnQD+JCI2AFcBH5L0piJD7Tl0nEv6uljb18XSDnHDul4eODBR5JBtc7Z8nC0fZ2s2Z2FFxH9ExPey28eAZ4CVRYYaf6XGQG/XzP2VPZ2MT9aKHLJtzpaPs+XjbM3mtYYl6WLgCuC7haTJRLQYu8gB58HZ8nG2fJytWduFJakX+ArwkYg42uL57ZKGJQ0ffnFhLbuqp4OxiemZ++OTJ+jv6VjQMReLs+XjbPk4W7O2CktSF/Wyuisivtpqm4jYGRGDETG44oKFhd50UTcjL0+z/+g0U7Xg3pEJtq3pWdAxF4uz5eNs+Thbs865NpAk4A7gmYj4bKFpMp1LxG1bVnDtroPUIrjp0j4uW77sXAw9J2fLx9nycbZmilYXoo0bSFuAfwF+AJzMHv54ROw60z6DG7tjz+6BRQtpZq8dm7eOMbz3eMvlsDlnWBHxCNVZ5zOz17DKvtPdzGw2F5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWjMoW1kPPT7JhaJT194xyyxNHyo7TxNnycbZ8nO20OQtLUrekPZL2Snpa0p8XHap2Mtjx6GEevK6fp65fzdDIMfYdmSp62LY4Wz7Olo+zNWtnhvUq8PaI2AhcDlwj6aoiQ+05dJxL+rpY29fF0g5xw7peHjgwUeSQbXO2fJwtH2drNmdhRd2pFF3Znygy1PgrNQZ6u2bur+zpZHyyVuSQbXO2fJwtH2dr1tYalqQOSU8Ch4BvRsR3iwwVLepQRQ44D86Wj7Pl42zN2iqsiKhFxOXAKmCzpDfP3kbSdknDkoYPv7iwll3V08HYxPTM/fHJE/T3dCzomIvF2fJxtnycrdm8XiWMiJeAh4FrWjy3MyIGI2JwxQULC73pom5GXp5m/9FppmrBvSMTbFvTs6BjLhZny8fZ8nG2Zp1zbSBpBTAdES9JOg/4deCWQkMtEbdtWcG1uw5Si+CmS/u4bPmyIodsm7Pl42z5OFszRasL0cYNpF8BPg90UJ+RfSkiPnW2fQY3dsee3QOLFtLMXjs2bx1jeO/xlsthc86wIuL7wBWLnsrMbJ4q+053M7PZXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMipbWA89P8mGoVHW3zPKLU8cKTtOE2fLx9nycbbT2i4sSR2SnpD09SIDAdROBjsePcyD1/Xz1PWrGRo5xr4jU0UP2xZny8fZ8nG2ZvOZYX0YeKaoII32HDrOJX1drO3rYmmHuGFdLw8cmDgXQ8/J2fJxtnycrVlbhSVpFfAbwO2FpsmMv1JjoLdr5v7Knk7GJ2vnYug5OVs+zpaPszVrd4Z1K/BR4OSZNpC0XdKwpOHDLy4sdESL4y/oiIvH2fJxtnycrdmchSXpXcChiHj8bNtFxM6IGIyIwRUXdCwo1KqeDsYmpmfuj0+eoL9nYcdcLM6Wj7Pl42zN2plhvRV4t6QDwBDwdklfLDLUpou6GXl5mv1Hp5mqBfeOTLBtTU+RQ7bN2fJxtnycrVnnXBtExM3AzQCSrgb+NCJuLDTUEnHblhVcu+sgtQhuurSPy5YvK3LItjlbPs6Wj7M1U7S6ED3TxqcL611n225wY3fs2T2wsGRm9pq0eesYw3uPt1wOm3OG1SgiHgYeXoRMZmbzVtl3upuZzebCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkzOvjZaxYW/svLzvCGe0++GTZEcw8wzKzdLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZlS2sh56fZMPQKOvvGeWWJ46UHadJlbNNXnCUx37vW/zT//gqo2/5YdlxmlT5vDlbPuc6W1uFJemApB9IelLScNGhaieDHY8e5sHr+nnq+tUMjRxj35GpoodtS5WzAXT9bCnrH7qcNd9ZX3aUJlU+b86WTxnZ5jPD+m8RcXlEDBaWJrPn0HEu6etibV8XSzvEDet6eeDARNHDtqXK2QCWvtLN6w4uRzWVHaVJlc+bs+VTRrZKXhKOv1JjoLdr5v7Knk7GJ2slJjqtytmqrMrnzdnyKSNbu4UVwDckPS5pe6sNJG2XNCxp+PCLCwsd0eL4Czri4qlytiqr8nlztnzKyNbuZ7q/NSIOSroI+KakZyPi240bRMROYCfA4MbuFj9K+1b1dDA2MT1zf3zyBP09HQs55KKpYraxTSOMX7kfgCvu2sKyifNKzdNKFc/bKc6WTxnZ2pphRcTB7O9DwH3A5iJDbbqom5GXp9l/dJqpWnDvyATb1vQUOWTbqpht4LF1XPXX7+Cqv35HJcsKqnneTnG2fMrINucMS1IPsCQijmW33wl8qtBQS8RtW1Zw7a6D1CK46dI+Llu+rMgh21blbACv9hxnz/Z/4sSyaRTi+atGeMtfvpPOqa65dy5Qlc+bs+VTRjZFqwvRxg2ktdRnVVAvuLsj4jNn22dwY3fs2T2wOAlfQ/w1X2aweesYw3uPt1wOm3OGFRHPARsXPZWZ2TxV8m0NZmatuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBlzfh5WroNKh4HRRTrchcBPFulYi83Z8nG2fKqabbFzrYmIFa2eKKSwFpOk4XPx1WJ5OFs+zpZPVbOdy1y+JDSzZLiwzCwZKRTWzrIDnIWz5eNs+VQ12znLVfk1LDOzU1KYYZmZAS4sM0tIZQtL0jWSfihpRNLHys7TSNKdkg5JeqrsLI0kDUj6Z0nPSHpa0ofLznSKpG5JeyTtzbL9edmZZpPUIekJSV8vO0sjSQck/UDSk5KGy87TSNL5kr4s6dnsv7u3FDpeFdewJHUAPwLeAbwAPAa8LyL2lRosI+ltwATwhYh4c9l5TpH0euD1EfE9Sb8IPA78ZhXOmyQBPRExIakLeAT4cET8a8nRZkj6Y2AQ6IuId5Wd5xRJB4DBiKjcm0YlfR74l4i4XdJS4Bci4qWixqvqDGszMBIRz0XEFDAEvKfkTDMi4tvAT8vOMVtE/EdEfC+7fQx4BlhZbqq6qJvI7nZlfyrzr6WkVcBvALeXnSUVkvqAtwF3AETEVJFlBdUtrJXAWMP9F6jIL14qJF0MXAF8t+QoM7JLrieBQ8A3I6Iy2YBbgY8CJ0vO0UoA35D0uKTtZYdpsBY4DPxtdil9u6SeIgesamGpxWOV+de46iT1Al8BPhIRR8vOc0pE1CLicmAVsFlSJS6nJb0LOBQRj5ed5QzeGhFXAtcCH8qWJKqgE7gS+L8RcQUwCRS63lzVwnoBGGi4vwo4WFKWpGTrQ18B7oqIr5adp5XssuFh4Jpyk8x4K/DubK1oCHi7pC+WG+m0iDiY/X0IuI/6kkkVvAC80DBT/jL1AitMVQvrMeCNkt6QLeS9F3ig5EyVly1s3wE8ExGfLTtPI0krJJ2f3T4P+HXg2VJDZSLi5ohYFREXU/9v7VsRcWPJsQCQ1JO9gEJ2ufVOoBKvTkfEfwJjki7NHvo1oNAXeDqLPHheEXFC0h8Cu4EO4M6IeLrkWDMk3QNcDVwo6QXgzyLijnJTAfWZwm8DP8jWigA+HhG7yos04/XA57NXgJcAX4qISr19oKJ+Cbiv/m8RncDdEfFQuZGa7ADuyiYWzwE3FTlYJd/WYGbWSlUvCc3Mfo4Ly8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNk/D8Ui/KXl/jvbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGZX9XMLT0dcP7Co7wox+5QuFflCSWUd8hGVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyKvv2MsdH9nBk6B6gRW/3JfQt3lJ2pHFVzvZH9x/kfz99nFcuqfPD31lbdpxJqjxvzpbPQmfr+AhLUl3So5LuLTIQQESLI0M7WNVzLWvOuIHB4UcZbj5b9LAdqXI2gP9wwTLu+82zy47xMlWeN2fLp4xsp3JK+H7g8aKCTHSiuY9GbSVd9ZVIDXq6N3B8ZPdCDD2rKmcDeNPqJaxYXC87xstUed6cLZ8ysnVUWJLOAd4G3FJomkyzdZRGrW98u1Hro9k6uhBDz6rK2aqsyvPmbPmUka3TI6xtwAeA1kx3kLRV0k5JO5vHBucj29QRCtjnfKlytiqr8rw5Wz7FZpu1sCS9HTgUEY+c7H4RsT0iNkbExnpvz5xC1WvLGW0NjG+Ptgao15bNaZ/zpcrZqqzK8+Zs+ZSRrZMjrDcA75T0FHAXsEXS7UWGWlTvZ7R1mJHmc0SMMji8i6Vd64scsmNVzlZlVZ43Z8unjGyzvqwhIm4EbgSQdBnwpxFxTZGhpDorllzFwcGbgaC3exPd9bOKHLJjVc4GcPW3nuX//HyIwy82WXv7k3xk40r+6FfL/41c5XlztnzKyFbZ12Et7VrH0q51ZceYVpWz3fnmavxnnk6V583Z8lnobKdUWBFxP3B/IUnMzGbhP80xs2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGZV9P6zT0VtXbyg7wsw+VXYAMx9hmVlCXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlozKvr3M8ZE9HBm6B2jR230JfYu3lB1pXJWzDa58nseu3MnzZw9w/rfXc+6Dryk70rgqz5uz5bPQ2ToqLElPAS8ATWA0IjYWGSqixZGhHazq2UqjtpwDL/w5S7supLt+VpHDJp8NoGuomwu+toFf/OqBsqNMUuV5c7Z0sp3KKeG/j4gNRZcVwInmPhq1lXTVVyI16OnewPGR3UUP25EqZwPoPr6Y5QdWoKbKjjJJlefN2fIpI1sl17CaraM0an3j241aH83W0fICTVDlbFVW5XlztnzKyNZpYQXwDUmPSNo63R0kbZW0U9LO5rHB+Uv40ggF7HO+VDlblVV53pwtn2Kzdbro/oaIOCDplcA3Je2JiO9MvENEbAe2Ayxa2x9zCVWvLWe0NTC+PdoaoF5bNpddzpsqZtu/aS8/u/hJAC6641IWHVtSap7pVHHexjhbPmVk6+gIKyIOZP8eAnYArysy1KJ6P6Otw4w0nyNilMHhXSztWl/kkB2rYrb+h89n82cuZ/NnLq9kWUE1522Ms+VTRrZZj7Ak9QC1iHghu/wW4GNFhpLqrFhyFQcHbwaC3u5NlXhWBKqdDeBEz4s8tPUfGF00gkLs27yX13/6LTSGu0rNVeV5c7Z8ysjWySnhKmCHpLH73xkRXys0FbC0ax1Lu9YVPUwuVc62aHAxb/zU28qOMa0qz5uz5bPQ2WYtrIh4Avi1BchiZnZSlXxZg5nZdFxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMRczp3Yyn36n0C+DpedrdmcDhedrXfHO2fJwtn6pmm+9c50bEK6a7oZDCmk+Sdi7ER4vl4Wz5OFs+Vc22kLl8SmhmyXBhmVkyUiis7WUHOAlny8fZ8qlqtgXLVfk1LDOzMSkcYZmZAS4sM0tIZQtL0hWSfiJpr6QPlp1nIkm3STok6cdlZ5lIUr+kf5T0uKTdkt5fdqYxkhZLekjSD7Jsf1Z2pqkk1SU9KunesrNMJOkpST+StEvSzrLzTCSpT9KXJO3J/t+9vtDxqriGJakO/BS4HHgGeBh4d0Q8VmqwjKQ3AceAz0fEa8vOM0bS2cDZEfF9SWcAjwC/VYV5U/uTeHsi4pikLuAB4P0R8d2So42T9CfARmBZRLy97DxjJD0FbIyIyr1oVNLngH+KiFskdQNLI2KgqPGqeoT1OmBvRDwREcPAXcCVJWcaFxHfAY6UnWOqiPh5RHw/u/wC8DiwptxUbdF2LNvsyr4q89tS0jnA24Bbys6SCknLgDcBtwJExHCRZQXVLaw1wP4J289QkR+8VEg6D7gI+F7JUcZlp1y7gEPANyOiMtmAbcAHgFbJOaYTwDckPSJpa9lhJvhl4BfAX2Wn0rdI6ilywKoWlqa5rjK/jatOUi/wZeC6iHi+7DxjIqIZERuAc4DXSarE6bSktwOHIuKRsrPM4A0RcTHwG8B7syWJKmgAFwP/KyIuAgaBQtebq1pYzwD9E7bPAQ6UlCUp2frQl4E7IuIrZeeZTnbacD9wRblJxr0BeGe2VnQXsEXS7eVGeklEHMj+PQTsoL1kUgXPAM9MOFL+Eu0CK0xVC+th4NWSXpUt5L0L+LuSM1VetrB9K/B4RHyy7DwTSXqFpL7s8hLgzcCeUkNlIuLGiDgnIs6j/X/t2xFxTcmxAJDUkz2BQna69RagEs9OR8SzwH5Jr8mu+nWg0Cd4GkXuPK+IGJX0x8DXgTpwW0TsLjnWOEl/A1wGnCnpGeAjEXFruamA9pHC7wE/ytaKAD4UEfeVF2nc2cDnsmeAa8AXI6JSLx+oqFXAjvbvIhrAnRHxtXIjTfI+4I7swOIJ4A+LHKySL2swM5tOVU8JzcxexoVlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTL+P9Lvpo4r3/kRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MO9j7Mg+v/TFj3aMoxLY1W3nHVy+iOtJZdLTSZivzc+ps+RSRrZUZ1gnA1yVVaKx5fSsi7mlrKmBh5+ks7Dy93cPk0j3Uwzuvf1/RMaZU5mxlfk6dLZ/5zjZtYUXEY8BZ85DFzOyI/F9zzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS0Zbvpfw3xz9Ig9/+P+0Y9ez9+GiA6Tpt77Z1i9KMmuJZ1hmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpkloy0fLzMXPn7/Tv7+mQO8YUGFxz60vOg4h3C2fA6MbmHP8N1Anb6uc+jvubDoSBOcLZ/5ztbyDEtSRdKjku5pZ6Bxf3DqIu597wnzMdSMOdvMRdTZM7ye43qvZNlRVzM08igjteeLjgU4W15FZJvJKeGngCfaFWSy809cwJKeynwNNyPONnMHa9uodiyls7IUqUpv10oOjG4uOhbgbHkVka2lwpJ0EvA+4Ka2prHXrFp9L9WO/ontakc/tfre4gI1cbZ8isjW6gzrBuAzQP1wd5C0VtJGSRtf3F2bi2z2mqeiAxyBs+XT3mzTFpak9wMvRMQjR7pfRKyLiFURserYpeU7JbFiVToWM1YfnNgeqw9S6VhUXKAmzpZPEdlamWGdC3xQ0tPAHcCFkm5tayp7zemuDDBW38VobTcRYwyNbGJh55lFxwKcLa8isk37toaIuAa4BkDSBcCfRcQVbU0FXPaj5/nH3wyz6+Uay299imtXLeXjbynHTxZnmzmpwpIFl7Jz6EYg6OtaTVfl+KJjAc6WVxHZSvs+rNvfVY4nZSrOls/CztNZ2Hl60TGm5Gz5zHe2GRVWRNwP3N+WJGZm0/B/zTGzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZpf08rHZ5z4kri46QpuuLDmDmGZaZJcSFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJKO3Hy3z8/p38/TMHeMOCCo99aHnRcQ4xtHQfj1+ykX0nDLLivjM5+aHTio40oczZDoxuYc/w3UCdvq5z6O+5sOhIE5wtn/nO1tIMS9LTkn4haZOkjW1NlPmDUxdx73tPmI+hZqxzuItTv7eSkx86tegor1LWbBF19gyv57jeK1l21NUMjTzKSO35omMBzpZXEdlmckr4HyJiZUSsaluaJuefuIAlPZX5GGrGug70sHjHElRT0VFepazZDta2Ue1YSmdlKVKV3q6VHBjdXHQswNnyKiKb17BsXtTqe6l29E9sVzv6qdX3FheoibPlU0S2VgsrgB9IekTS2qnuIGmtpI2SNr64uzZ3Ce01rFyzwEM5Wz7tzdbqovu5EbFD0huAH0raEhEPNN8hItYB6wBWva0n5jhn4bav3spzZz8FwFm3nUf3/gUFJ3pFmbONq3QsZqw+OLE9Vh+k0rGouEBNnC2fIrK1VFgRsSP7/QVJ64G3Aw8c+VGvLQMbVjCwYUXRMaZU5mzjuisDjNV3MVrbTbVjMUMjmzi29/KiYwHOllcR2aYtLEm9QEdEvJRdvgj4YltTAZf96Hn+8TfD7Hq5xvJbn+LaVUv5+FvK8ZPlYO/LPLz2x4x1j6IQ29Zs5R1fvYjqSGfR0UqbTaqwZMGl7By6EQj6ulbTVTm+0EzjnC2fIrIp4shnb5LeBKzPNqvA7RHx5SM9ZtXbeuLh7w/MTcI55q/5ymfr9WuKjmCvEzuuu4GD27ZPuRg27QwrIp4E3jbnqczMZshvazCzZLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZ034eVq6dSi8Cz8zR7o4Bds3Rvuaas+XjbPmUNdtc5zo5Io6d6oa2FNZckrRxvr5abKacLR9ny6es2eYzl08JzSwZLiwzS0YKhbWu6ABH4Gz5OFs+Zc02b7lKv4ZlZjYuhRmWmRngwjKzhJS2sCRdLOlXkrZK+mzReZpJukXSC5J+WXSWZpIGJP2DpCckbZb0qaIzjZPUI+lhST/Psv3XojNNJqki6VFJ9xSdpZmkpyX9QtImSRuLztNMUr+kOyVtyf7evaOt45VxDUtSBfg18G7gWWAD8NGIeLzQYBlJ5wP7gW9ExFuLzjNO0gnACRHxM0lHAY8Av1uG4yZJQG9E7JfUCTwIfCoiflJwtAmS/hRYBSyKiPcXnWecpKeBVRFRujeNSvo68E8RcZOkLmBhRAy2a7yyzrDeDmyNiCcjYgS4A7ik4EwTIuIBYE/ROSaLiN9ExM+yyy8BTwDLik3VEA37s83O7FdpflpKOgl4H3BT0VlSIWkRcD5wM0BEjLSzrKC8hbUM2N60/Swl+YeXCkmnAGcBPy04yoTslGsT8ALww4goTTbgBuAzQL3gHFMJ4AeSHpG0tugwTd4EvAj8VXYqfZOk3nYOWNbC0hTXleancdlJ6gO+DVwVEfuKzjMuImoRsRI4CXi7pFKcTkt6P/BCRDxSdJbDODcizgZ+B/hEtiRRBlXgbOB/R8RZwBDQ1vXmshbWs8BA0/ZJwI6CsiQlWx/6NnBbRHyn6DxTyU4b7gcuLjbJhHOBD2ZrRXcAF0q6tdhIr4iIHdnvLwDraSyZlMGzwLNNM+U7aRRY25S1sDYAb5b0xmwh7yPA3xWcqfSyhe2bgSci4itF52km6VhJ/dnlBcC7gC2FhspExDURcVJEnELj79p9EXFFwbEAkNSbvYBCdrp1EVCKV6cj4nlgu6TTsqt+G2jrCzzVdu48r4gYk/THwPeBCnBLRGwuONYESX8DXAAcI+lZ4NqIuLnYVEBjpvAx4BfZWhHA5yLi3uIiTTgB+Hr2CnAH8K2IKNXbB0rqOGB942cRVeD2iPhesZEO8Ungtmxi8STwR+0crJRvazAzm0pZTwnNzF7FhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMv4/tzXpaJr7EJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#play against difficulty=2\n",
    "lastE_medium_last_episode=info['episodes_all'][num_episodes-1][2][99]\n",
    "dummy_g=connect4()\n",
    "for i in lastE_medium_last_episode:\n",
    "    dummy_g.display_colour(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "60619f5b",
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#play against difficulty=0\n",
    "lastE_0_last_episode=info['episodes_all'][num_episodes-1][0][99]\n",
    "dummy_g=connect4()\n",
    "for i in lastE_0_last_episode:\n",
    "    dummy_g.display_colour(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618f3975",
   "metadata": {},
   "source": [
    "## 8.a Intensive training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d29ffb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_episodes = 100001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef8d2bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to generate Qs and policies and then train\n",
    "def gen_train(double=False, upgrade=False, prioritized=False, alpha=0.8, beta=0.1, h=128, num_episodes=2501):\n",
    "    config={'double':double, 'prioritized':prioritized, 'alpha':alpha, 'beta':beta}\n",
    "    Q_network = DQN(42, h, 7).to(device)\n",
    "    Q_target = DQN(42, h, 7).to(device)\n",
    "    if not double: \n",
    "        Q_target.load_state_dict(Q_network.state_dict()) \n",
    "    policy = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_network, upgrade=upgrade)\n",
    "    policyB = E_Greedy_Policy(0.99, decay=0.99999, min_epsilon=0.1, Q_network=Q_target, upgrade=upgrade)\n",
    "    Q_network, Q_target, info = train(Q_network, Q_target, policy, policyB, config, num_episodes)\n",
    "    return Q_network, Q_target, info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b685c63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9899901  loss: tensor(2.3021)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 78 win random, 0 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9655470366458386  loss: tensor(1.7765)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 66 win random, 3 win basic, 3 win medium\n",
      "Training at episode 5000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9417074776561513  loss: tensor(1.3464)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 72 win random, 5 win basic, 2 win medium\n",
      "Training at episode 7500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.918456522381508  loss: tensor(1.8163)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 69 win random, 4 win basic, 5 win medium\n",
      "Training at episode 10000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8957796380726498  loss: tensor(1.1982)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 71 win random, 6 win basic, 3 win medium\n",
      "Training at episode 12500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8736626507969416  loss: tensor(2.0256)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 67 win random, 4 win basic, 2 win medium\n",
      "Training at episode 15000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.8520917365791183  loss: tensor(0.2418)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 56 win random, 2 win basic, 1 win medium\n",
      "Training at episode 17500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8310534127607676  loss: tensor(1.4503)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 76 win random, 2 win basic, 4 win medium\n",
      "Training at episode 20000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8105345295731438  loss: tensor(1.3148)\n",
      "average rewards unscaled in previous 10 games: -100.0\n",
      "300 games greedy evaluation: 73 win random, 1 win basic, 4 win medium\n",
      "Training at episode 22500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.79052226191805  loss: tensor(3.4362)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 74 win random, 1 win basic, 4 win medium\n",
      "Training at episode 25000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7710041013516606  loss: tensor(1.2540)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 65 win random, 0 win basic, 0 win medium\n",
      "Training at episode 27500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7519678482662441  loss: tensor(0.9713)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 68 win random, 3 win basic, 2 win medium\n",
      "Training at episode 30000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7334016042649502  loss: tensor(1.4574)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 71 win random, 4 win basic, 3 win medium\n",
      "Training at episode 32500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7152937647248447  loss: tensor(3.0583)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 64 win random, 1 win basic, 2 win medium\n",
      "Training at episode 35000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6976330115435653  loss: tensor(3.5818)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 63 win random, 6 win basic, 5 win medium\n",
      "Training at episode 37500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6804083060650827  loss: tensor(1.0578)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 64 win random, 4 win basic, 0 win medium\n",
      "Training at episode 40000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6636088821800907  loss: tensor(2.3764)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 65 win random, 3 win basic, 3 win medium\n",
      "Training at episode 42500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6472242395967841  loss: tensor(1.3472)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 69 win random, 2 win basic, 5 win medium\n",
      "Training at episode 45000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6312441372777715  loss: tensor(2.5299)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 65 win random, 1 win basic, 2 win medium\n",
      "Training at episode 47500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6156585870390102  loss: tensor(1.4119)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 71 win random, 3 win basic, 3 win medium\n",
      "Training at episode 50000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6004578473068333  loss: tensor(2.2769)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 68 win random, 1 win basic, 0 win medium\n",
      "Training at episode 52500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.5856324170290721  loss: tensor(1.2919)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 59 win random, 1 win basic, 4 win medium\n",
      "Training at episode 55000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.5711730297365227  loss: tensor(2.4333)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 76 win random, 4 win basic, 3 win medium\n",
      "Training at episode 57500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.5570706477510474  loss: tensor(1.1590)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 71 win random, 4 win basic, 1 win medium\n",
      "Training at episode 60000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5433164565366881  loss: tensor(3.7536)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 73 win random, 4 win basic, 3 win medium\n",
      "Training at episode 62500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5299018591902234  loss: tensor(2.3683)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 66 win random, 7 win basic, 3 win medium\n",
      "Training at episode 65000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5168184710677814  loss: tensor(1.0298)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 65 win random, 3 win basic, 2 win medium\n",
      "Training at episode 67500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5040581145441035  loss: tensor(1.7149)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 62 win random, 4 win basic, 1 win medium\n",
      "Training at episode 70000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4916128139012161  loss: tensor(0.8598)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 74 win random, 1 win basic, 3 win medium\n",
      "Training at episode 72500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.47947479034329876  loss: tensor(1.8115)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 71 win random, 4 win basic, 0 win medium\n",
      "Training at episode 75000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.4676364571346298  loss: tensor(2.5992)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 69 win random, 5 win basic, 2 win medium\n",
      "Training at episode 77500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4560904148575822  loss: tensor(0.8471)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 67 win random, 3 win basic, 2 win medium\n",
      "Training at episode 80000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.4448294467877098  loss: tensor(0.6025)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 53 win random, 1 win basic, 5 win medium\n",
      "Training at episode 82500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.43384651438300365  loss: tensor(1.8267)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 68 win random, 4 win basic, 1 win medium\n",
      "Training at episode 85000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4231347528845336  loss: tensor(0.1479)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 67 win random, 7 win basic, 4 win medium\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training at episode 87500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.41268746702571074  loss: tensor(2.0882)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 70 win random, 1 win basic, 3 win medium\n",
      "Training at episode 90000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.4024981268474819  loss: tensor(1.6999)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 63 win random, 1 win basic, 3 win medium\n",
      "Training at episode 92500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.3925603636168549  loss: tensor(2.9058)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 69 win random, 2 win basic, 1 win medium\n",
      "Training at episode 95000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.3828679658461905  loss: tensor(0.7395)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 65 win random, 1 win basic, 3 win medium\n",
      "Training at episode 97500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.3734148754107833  loss: tensor(1.4661)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 66 win random, 1 win basic, 1 win medium\n",
      "Training at episode 100000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.36419518376229976  loss: tensor(3.3935)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 61 win random, 2 win basic, 1 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  100001  episodes done in 6441.62 seconds\n"
     ]
    }
   ],
   "source": [
    "#vanila DQN, upgrade=False\n",
    "Q_network_v, Q_target_v, info_v = gen_train(double=False, upgrade=False, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44688784",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9899901  loss: tensor(1.4617)\n",
      "average rewards unscaled in previous 10 games: 100.0\n",
      "300 games greedy evaluation: 76 win random, 1 win basic, 0 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9655470366458386  loss: tensor(0.7597)\n",
      "average rewards unscaled in previous 10 games: 50.0\n",
      "300 games greedy evaluation: 61 win random, 3 win basic, 3 win medium\n",
      "Training at episode 5000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9417074776561513  loss: tensor(0.8571)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 74 win random, 2 win basic, 2 win medium\n",
      "Training at episode 7500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.918456522381508  loss: tensor(1.8865)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 67 win random, 4 win basic, 2 win medium\n",
      "Training at episode 10000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8957796380726498  loss: tensor(0.9283)\n",
      "average rewards unscaled in previous 10 games: -70.0\n",
      "300 games greedy evaluation: 63 win random, 2 win basic, 3 win medium\n",
      "Training at episode 12500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.8736626507969416  loss: tensor(1.9499)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 76 win random, 2 win basic, 6 win medium\n",
      "Training at episode 15000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.8520917365791183  loss: tensor(2.2090)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 65 win random, 7 win basic, 0 win medium\n",
      "Training at episode 17500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8310534127607676  loss: tensor(1.4242)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 71 win random, 6 win basic, 3 win medium\n",
      "Training at episode 20000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8105345295731438  loss: tensor(2.2802)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 62 win random, 6 win basic, 1 win medium\n",
      "Training at episode 22500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.79052226191805  loss: tensor(0.3488)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 74 win random, 2 win basic, 6 win medium\n",
      "Training at episode 25000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7710041013516606  loss: tensor(1.5064)\n",
      "average rewards unscaled in previous 10 games: -70.0\n",
      "300 games greedy evaluation: 70 win random, 2 win basic, 1 win medium\n",
      "Training at episode 27500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7519678482662441  loss: tensor(0.2661)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 67 win random, 2 win basic, 3 win medium\n",
      "Training at episode 30000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7334016042649502  loss: tensor(1.4048)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 63 win random, 5 win basic, 2 win medium\n",
      "Training at episode 32500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7152937647248447  loss: tensor(2.3174)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 66 win random, 3 win basic, 4 win medium\n",
      "Training at episode 35000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6976330115435653  loss: tensor(0.8667)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 66 win random, 2 win basic, 4 win medium\n",
      "Training at episode 37500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6804083060650827  loss: tensor(0.9899)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 63 win random, 5 win basic, 5 win medium\n",
      "Training at episode 40000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6636088821800907  loss: tensor(0.7734)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 58 win random, 3 win basic, 2 win medium\n",
      "Training at episode 42500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6472242395967841  loss: tensor(1.6572)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 66 win random, 5 win basic, 2 win medium\n",
      "Training at episode 45000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6312441372777715  loss: tensor(0.7217)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 64 win random, 6 win basic, 2 win medium\n",
      "Training at episode 47500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6156585870390102  loss: tensor(1.3195)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 80 win random, 7 win basic, 3 win medium\n",
      "Training at episode 50000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6004578473068333  loss: tensor(2.0303)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 56 win random, 4 win basic, 1 win medium\n",
      "Training at episode 52500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.5856324170290721  loss: tensor(1.6676)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 68 win random, 1 win basic, 1 win medium\n",
      "Training at episode 55000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5711730297365227  loss: tensor(3.1276)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 65 win random, 10 win basic, 5 win medium\n",
      "Training at episode 57500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5570706477510474  loss: tensor(0.6695)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 74 win random, 5 win basic, 4 win medium\n",
      "Training at episode 60000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5433164565366881  loss: tensor(3.0104)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 71 win random, 7 win basic, 4 win medium\n",
      "Training at episode 62500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5299018591902234  loss: tensor(2.4861)\n",
      "average rewards unscaled in previous 10 games: 50.0\n",
      "300 games greedy evaluation: 55 win random, 2 win basic, 3 win medium\n",
      "Training at episode 65000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5168184710677814  loss: tensor(2.5499)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 63 win random, 2 win basic, 1 win medium\n",
      "Training at episode 67500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.5040581145441035  loss: tensor(1.3620)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 61 win random, 3 win basic, 2 win medium\n",
      "Training at episode 70000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4916128139012161  loss: tensor(0.8314)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 66 win random, 6 win basic, 5 win medium\n",
      "Training at episode 72500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.47947479034329876  loss: tensor(1.9440)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 64 win random, 3 win basic, 3 win medium\n",
      "Training at episode 75000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.4676364571346298  loss: tensor(1.0549)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 57 win random, 1 win basic, 2 win medium\n",
      "Training at episode 77500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.4560904148575822  loss: tensor(1.5487)\n",
      "average rewards unscaled in previous 10 games: 50.0\n",
      "300 games greedy evaluation: 71 win random, 0 win basic, 5 win medium\n",
      "Training at episode 80000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4448294467877098  loss: tensor(0.1462)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 72 win random, 3 win basic, 3 win medium\n",
      "Training at episode 82500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.43384651438300365  loss: tensor(0.7170)\n",
      "average rewards unscaled in previous 10 games: 80.0\n",
      "300 games greedy evaluation: 67 win random, 4 win basic, 5 win medium\n",
      "Training at episode 85000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4231347528845336  loss: tensor(2.6516)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 62 win random, 2 win basic, 2 win medium\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training at episode 87500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.41268746702571074  loss: tensor(1.6648)\n",
      "average rewards unscaled in previous 10 games: 30.0\n",
      "300 games greedy evaluation: 66 win random, 5 win basic, 4 win medium\n",
      "Training at episode 90000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.4024981268474819  loss: tensor(1.4166)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 67 win random, 1 win basic, 4 win medium\n",
      "Training at episode 92500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.3925603636168549  loss: tensor(0.5205)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 66 win random, 2 win basic, 1 win medium\n",
      "Training at episode 95000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.3828679658461905  loss: tensor(1.1614)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 75 win random, 4 win basic, 2 win medium\n",
      "Training at episode 97500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.3734148754107833  loss: tensor(0.9841)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 70 win random, 1 win basic, 6 win medium\n",
      "Training at episode 100000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.36419518376229976  loss: tensor(0.6128)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 68 win random, 2 win basic, 0 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  100001  episodes done in 8919.19 seconds\n"
     ]
    }
   ],
   "source": [
    "#vanila DQN, upgrade=True\n",
    "Q_network_v_up, Q_target_v_up, info_v_up = gen_train(double=False, upgrade=True, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f8228052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9899901  loss: tensor(0.4012)\n",
      "average rewards unscaled in previous 10 games: 100.0\n",
      "300 games greedy evaluation: 72 win random, 0 win basic, 1 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9778681856754372  loss: tensor(0.2778)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 64 win random, 4 win basic, 3 win medium\n",
      "Training at episode 5000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9650547307442935  loss: tensor(2.2196)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 63 win random, 1 win basic, 0 win medium\n",
      "Training at episode 7500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9534669428110791  loss: tensor(1.7294)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 64 win random, 0 win basic, 0 win medium\n",
      "Training at episode 10000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9419429351338119  loss: tensor(1.6209)\n",
      "average rewards unscaled in previous 10 games: 50.0\n",
      "300 games greedy evaluation: 73 win random, 6 win basic, 9 win medium\n",
      "Training at episode 12500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9298605510313548  loss: tensor(2.7174)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 62 win random, 0 win basic, 0 win medium\n",
      "Training at episode 15000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9182820313469726  loss: tensor(1.9070)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 58 win random, 5 win basic, 1 win medium\n",
      "Training at episode 17500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9063671817647277  loss: tensor(2.5504)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 72 win random, 5 win basic, 5 win medium\n",
      "Training at episode 20000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8960842564548778  loss: tensor(1.5684)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 54 win random, 1 win basic, 0 win medium\n",
      "Training at episode 22500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.884271708904442  loss: tensor(2.2412)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 65 win random, 3 win basic, 0 win medium\n",
      "Training at episode 25000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8743006605673076  loss: tensor(1.6788)\n",
      "average rewards unscaled in previous 10 games: 60.0\n",
      "300 games greedy evaluation: 63 win random, 2 win basic, 1 win medium\n",
      "Training at episode 27500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8635780314906907  loss: tensor(2.3914)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 66 win random, 0 win basic, 0 win medium\n",
      "Training at episode 30000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8514699334881383  loss: tensor(1.6745)\n",
      "average rewards unscaled in previous 10 games: -80.0\n",
      "300 games greedy evaluation: 72 win random, 4 win basic, 2 win medium\n",
      "Training at episode 32500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.842256108640158  loss: tensor(2.3235)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 67 win random, 2 win basic, 0 win medium\n",
      "Training at episode 35000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.830172958464184  loss: tensor(1.7307)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 71 win random, 6 win basic, 3 win medium\n",
      "Training at episode 37500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.8200407231253991  loss: tensor(2.0393)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 65 win random, 1 win basic, 1 win medium\n",
      "Training at episode 40000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8110696616124717  loss: tensor(2.2811)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 59 win random, 0 win basic, 0 win medium\n",
      "Training at episode 42500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8010103625272743  loss: tensor(2.1742)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 60 win random, 0 win basic, 0 win medium\n",
      "Training at episode 45000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7914872924516757  loss: tensor(3.1445)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 63 win random, 0 win basic, 0 win medium\n",
      "Training at episode 47500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7816786806704659  loss: tensor(2.5170)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 58 win random, 0 win basic, 1 win medium\n",
      "Training at episode 50000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.770264288882102  loss: tensor(1.3835)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 64 win random, 1 win basic, 4 win medium\n",
      "Training at episode 52500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7620130141067872  loss: tensor(2.6358)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 65 win random, 0 win basic, 1 win medium\n",
      "Training at episode 55000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7526524538795075  loss: tensor(2.1911)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 58 win random, 0 win basic, 1 win medium\n",
      "Training at episode 57500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7418844479914083  loss: tensor(2.7225)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 63 win random, 3 win basic, 1 win medium\n",
      "Training at episode 60000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7327345090439521  loss: tensor(1.6625)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 72 win random, 4 win basic, 0 win medium\n",
      "Training at episode 62500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7237263683433358  loss: tensor(0.7968)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 72 win random, 2 win basic, 4 win medium\n",
      "Training at episode 65000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7142644778216344  loss: tensor(1.6513)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 67 win random, 3 win basic, 1 win medium\n",
      "Training at episode 67500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7074544621325556  loss: tensor(1.9674)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 67 win random, 0 win basic, 0 win medium\n",
      "Training at episode 70000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6984916323844378  loss: tensor(0.5161)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 57 win random, 0 win basic, 0 win medium\n",
      "Training at episode 72500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6884089920639798  loss: tensor(2.2126)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 65 win random, 1 win basic, 7 win medium\n",
      "Training at episode 75000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6812184779568304  loss: tensor(2.0804)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 59 win random, 1 win basic, 0 win medium\n",
      "Training at episode 77500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6728571255730326  loss: tensor(1.5350)\n",
      "average rewards unscaled in previous 10 games: -80.0\n",
      "300 games greedy evaluation: 61 win random, 0 win basic, 0 win medium\n",
      "Training at episode 80000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6643924067672804  loss: tensor(1.6056)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 61 win random, 0 win basic, 0 win medium\n",
      "Training at episode 82500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6563622770873448  loss: tensor(2.4449)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 58 win random, 1 win basic, 0 win medium\n",
      "Training at episode 85000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6483060125147512  loss: tensor(2.8484)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 66 win random, 1 win basic, 0 win medium\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training at episode 87500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6380666511758712  loss: tensor(3.3629)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 60 win random, 2 win basic, 3 win medium\n",
      "Training at episode 90000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6299514058351461  loss: tensor(2.1349)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 76 win random, 4 win basic, 2 win medium\n",
      "Training at episode 92500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.624613238797382  loss: tensor(4.2060)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 68 win random, 1 win basic, 1 win medium\n",
      "Training at episode 95000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.614188912628111  loss: tensor(2.2587)\n",
      "average rewards unscaled in previous 10 games: -40.0\n",
      "300 games greedy evaluation: 67 win random, 7 win basic, 0 win medium\n",
      "Training at episode 97500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.606480449533134  loss: tensor(3.2406)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 64 win random, 6 win basic, 2 win medium\n",
      "Training at episode 100000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.602063220004848  loss: tensor(3.0761)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 66 win random, 1 win basic, 0 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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h/G1gy+zmpmMGNMfVRfhmhckV5Gbr8XNExacwooBioZz8vDx4enBLFu87xaqDRb+hqmxyDi2Rl1u6+hNyOoHdBeR8rwSlkSMYCWxT1axZHU6JSAMA+/m0sxNQGtF7/p4Iqrm7MqDVxRmcfi3rERmXzOGo4mXN0zIy2RseS6fGtWnh6wlASBkGgj0nY1m6/zSP9G2WaxO627r509G/Fh/MD+b8JRX1odHneX3OHro3rcPzQwOzl/t5eXBn98bM2hZWYcpeS9L83ZFUcXVhcJtcM9F5eqRfM5p6V+ftP/aaTn8OSEnP4HBUAm1yaTGU07UdGlDFzaVCVBqXRiC4mwvFQgBzgQfs1w8Av5dCGpwavTMzlYV7TzGojc9FRSRgBQKANYeKdzd28FQ8yWmZdPSvhZeHO/W9PDh0uuwqjL9aFkJNDzce6BuQ6+cuLsLYG9pzKi7lonL/1PRMnp2+HReBz+7qfFlHvycGtkAVvltpKjhzUlUW7o3k6sB6he6gWNXNlbeub8fhqPNMWXfMOQmsQA6dSiA9UwvMEdSq5s6wtn7M3Rl+xQdYpwYCEakODANm51j8ITBMRA7Zn33ozDRkcWb0vpBlb3DZZ43rVqdJ3eqsCSlePUFWs9HOjWsD0NLXs8xyBAci41mwN5KHrgrAK5+LUremdbilayMmrD7KMXuoj08WHWBnWCz/vq1jrh3iGtWuxq1d/fl584kKOwlIUewKi+VkTBIjO1z+G3PE4Da+DGztw+dLDhGVT49k48LQEgUFArD6FJw9n8pH8/df0XVbTg0Eqpqoqt6qGptjWbSqDlHVQPv5rDPTkMWZ0Xv+7kiquOWdZe/bsh4bjkQXa6CqnSdiqF3dnSZ1rYtnViAoi4qqr5aHUKOKKw/3a1bguq+NaIO7q/Den/tYceA041cdYXSvJrkGzSxPDWpBRqby3aojJZnsK9pfeyJwcxGGFbGXuojw5vXtSErL4OOF+0s4dRXL/sh4PNxdCMil1/al+rfyYVBrHyasOUr/j5dz01drmLD6CBGxV1bRZoXuWXyprOi98kDJVZqpKgv2RNA/0CfP7ub9WtYjISWdnWFFL5baGRZLh0a1socLCPTzJDE1g/DY0r1rPhyVwLxd4dzXJ4Da1asUuL6vlwfPDAlkSfBp/jZ1G639avLm9e3y3aapdw1u6tSQqRtDOWN6G9u/sUj6tPDOsxWLI1r4ePJwv2b8ujWMdSFnOB2XnOvj3PnUEkz9lSc4Io7W9b1wdSl4aA53VxcmPdSTNa8O4h8j25Chynt/BtPng2Xc/r91TFl37IrIgVWMgTIc1L+VD941qjB7exhD25XM+D87w2IJj03mpeGt81ynTwtvRKyB2bo1LXxH6qTUDA6eimdo2xbZy1r6WBXGh07F06h2tcInvIi+Xh5CVTcXHr264NxAlof6BjBj8wkiYpP46p4ueLjn3SM2y1ODWjJnx0kmrD7KayPbFCfJV7zgiHhCoxMZ079FwSsX4JnBLZm97ST3TNiY73rj7+vG8PaOt06qKFSV4Ig4rinkufvXqc6YAS0YM6AFR8+cZ97OcObtimDs3L18OH8/Ex/sXq4HY6xUgcDd1YUbOjVk2qbjxCamFevuKst8O8s+NJ8se90aVWjf0Is1IWd4dkhgnuvlZW94LBmZSkf/2tnLAv2sFg0hpxMY2LpwrUiK6nh0Ir/vCOeBPgHU86zq8HZV3Vz56dFexCamZae7IC19PbmuQwN+XH+MJwY0dyj3UVEt2BOBi8Dw9sW/eanp4c7MJ/rk28nxk4UH+Gt3RKUMBKfiUjiXmOZQ/UBemtWrwTNDAnlmSCAHIuN5Zvo2Hpm8hSkP96Rns7olmNqSU6mKhgBu7epPanomf5bAkBNZWfarWtYrMKj0a+nD9uPnLmtK6YgdJ2IA6OR/YUjhujWqULdGlVKtMP56eQiuLsKYAc0LvW2j2tVo17Bw/1xPD27J+dQMvl97rNDHq0j+2hNJz2Z1CxV88xNQrwajezXN8zGotS8rD0aVi57rJWXlwSgORBbcyq4wFcWOaF2/Jj892osGtT14aNImth0/5/C2sYlpvPPHPuKT00okLfmpdIEgqJEXLX09S2S6uX0RcYRGJxY47gtY9QRpGcqmY4WvG98VFkuDWh74enlctLw0Ww5NXnuUGVtOcG+vpvhdkg5naVPfi+Ht/Ji09ihxpfDPUB6FnI4n5HQCI/OpXC9pg9r4ci4xjR0nHL9olWfxyWk8/sMWnpm+jcwCgltwpBUICupDUBi+NT2Y9mhv6tWsygPfb2KXPVRMflYdjOKaz1YxZf0xNhxxfnuaShcIRIRbujZi87FzxR5/ZcGeSCvL7kB9Q/eAOlRxc2FtEWYt2xkWQ6ccxUJZAn09OVQKLYembgxl3B/7GN7Oj39cW7rl9c8MDiQ+OZ0fKmn79/m7rdnuCltmXRz9W/ng6iIs2+/0vp6lYv6eSFLSMzl4KoFF+07lu25wRDz+darl2yy6KOrX8mDaY72pVc2d+yZuYm947g1Hzqek88Zvu7n/+014ergx56mrGFZC9Zn5qXSBAODmzo0QodgD0c3fE0mvZt54O5Bl93B3pUdAnULPTxCTmEpodCIdG18+01RLX09ik9KIcrBlzfmUdO4av56vl4c43JT1l80neH3OHga38eWre7qW+oQzHfxrMai1DxPXHC1SsVpZiIhNYsLqI9z67To+XXSgWPuavyeSrk1qU79W6eTCwGpq3a1pHZbtL1zrOlXliR+3Fno+CmebvS2MAO/qNKtXgy+XHcr3xsmag6BkioUu1ah2NaY/1psaVVy5b+Kmy4qqthw7y7VfrGbqxuM82q8Z857pd1G9oDNVykDQsHY1+jT3LtaQE9lZ9g6O36n1bVmP/ZHxhWpOltXktHOuOYILFcaOWH84mg1HzvLxwgPc/t16jhQw5POc7WG8OnsXVwfW45vRXaniVjY/l2eGBHIuMY2pG8vXBSanqPgUflh/jNv/t44+HyzjvT+DORyVwP9WHeFsEZtjhkafZ19EHNcWsRNZcQxu40twRFyh2sPvPhnLgr2RfLzwQKmUazsi7FwiG46c5Zau/jw5sAV7w+NYfiD3nE5yWgZHohJoW7/kioUu1bhudaY91ht3V2H0hA2EnE4gOS2DD+YHc/t368nIVH5+rDdvXN/OodZ1JaVSBgKwhpwIjU5k/ZGi9fidaw+mVpgse9ZwE+sKMSz1LruiOMg/9xwBOB4I1oScwcPdhf/c3okjUee59ovVTF57NNdy03m7wnnpl530ae7N/93fvVR/lJfq2qQOfVt6M37VUZLTMsosHZdKz8jk503HGT1hA73+tYS3ft9LXFI6Lw1rxfKXB/LLmD6kpmfyy5YTRdr//D2lXyyUJatz5PJC5ApmbzuJm4sQm5TGj+UkV5A16OGoLo0Y1aUR/nWq8cXSkFxvAA+eiidTS66iOC8B9Wow9dHegHDP/23gpq/W8t3KI9zVozELnu9Pr+beTj1+biptIBgRVJ9a1dwZPWEjd3y3nh83FNx56UhUAl8sPcTw/67ki2UhXNXCu1AVp+0b1qJWNXfWFKKeYGdYDC18auRaZunnVZWaVd0cDgRrQ87Qs5k3t3bzZ9EL/end3Jtxf+zj3okbLxrkbcGeSJ77eQfdm9ZlwgNlGwSyPDM4kDMJKUzfdLysk5Lti2UhvDZ7N+ExyTw9qCWLXujPwhf688yQQJrVq0Erv5r0alaXnzaEFqkFzvw9kXRoVIvGdUt/bupAX08a1a6W593zpdIyMpm7M5xrgurTv5UPE1YfLfOZ0VSVWdvC6BlQl8Z1q+Pu6sKTA1uw40RMrkW0+/OZjKaktfT1ZNpjvUjPVM4lpjLpwR58cEvHMpsDudIGAs+qbsx7ph/PD2lFdEIKb/62h57vL+G+iRuZsfk4MYlWdv7E2US+XXGY675YzeD/rOTTxQepXa0K797Unm/v7VaoY7q6CFe18HZ4WGpVZceJ2FwrisGq+G7p5+nQ/MWn4pI5dDqBfi2tuw0/Lw/7x9eBnSdiGPHfVfy65QRLg0/xzPRtdPKvxfcP9XB43Htn693cm54Bdflu5RFS0ss+VxCblMakNUcZ0b4+y14awIvDW9Mqlz4S9/cJIOxcEiscvKBmCY9Jsv4uhRhyuiSJCIPb+LI25IxD3/fKA1GcPZ/KLV0a8ezglpw9n8q0jWUbtHeGxXIk6jy3dG2Uvey2bv40qOXBl0svH+p8X0QcNaq4Zg/j4myt/Gqy9MUBLH95IIMKOaJsSSsf/+VlpHHd6jw3NJBnh7Rkf2Q883aF88fOCF6dtZs3fttDgHeN7DH/OzeuzRvXteW6jg1oUKvoPXn7tqzH/D2RHD1znuZ27+C8RMQmcyYhhU72QHO5aenjyXIHhszImjs5a9Y0sP7Z7+7ZhL4t6vHyzJ38feYuRKBDo1pMfrhnmd2d5OWZIS25b+ImZm4NY3SvpgVv4IC/dkdw4mwiYwYUrtfuD+uOEZ+SzjNDWmYP+5Gb4e398POqyg/rQxlSiHGC/rL7uTjSNNlZBrfx5ccNoWw8cpb+rfKfE2T29jC8a1Shfysf3F1d6NPcm+9WHeHe3k3LLEc5Z1sYVdxcLhqor6qbK2P6N2fcH/vYcCSa3jmKYayhJWri4sDQEiWlTo3y0VGy0uYIchIR2jbw4u/XtGHl3wcy9+m+PNS3GT41q/LqiDasfmUQv/2tL49e3bxYQQAu1BM4Mql9VnvjjrnUD2QJ9PPkTEJKdg4mL2tCzlC3RhXa5jIZdxPv6lYF1XVtGdG+Pj8+3KvEm8+VhH4t69G5cW2+XXG4RAYOnLU1jL9N28YH8/c79PfIkpCSzsS1RxnSxpf2DfP+24DVm/3unk1YeTAqewTWgsQnp/G/lUfo1rROgTcLztSnhTdV3VwKbEYam5jGkuDT3Ni5YXarsmeGtCQqPqXI9SPFlZpuFVUNa+dHrWoX/5bv6tmEep5V+XLZoexlWUNLtCmFYqHyyASCS4gIHf1r889r2zLtsd48ObBFiZbRNvWuTqPa1RxqRrrjRCzurpJvmaUjFcaqytqQM1zVwjvPux0XF+HRq5vz7b3dSmToDWcQEZ4d0pKwc0nFbvo7d2c4f59pVYY3rlutUJO2/LQhlJjENJ5xcLiQe3o2wc1FHG5W+dWyEM4kpPBWAYPzOZuHuytXtfBm+YHT+RZl/rk7gtT0TG7p4p+9rE9zb7o3rcP/VhwmNb30x+pfeTCKc4lp3JqjWCiLh7srj/dvxtqQaLaGWp3mwmOTiUtOL5X6gfLIBIJSJiJcHViPNYfOEFnAyKG7wmJoU98r36x1VhPS/KatPByVwKm4lOzcyJVsUGtf2jf04ptC9IW41PzdEbwwYwfdA6zK8Deua8fBUwkOXaiTUjOYsPoIVwfWy54boiC+Xh5cE1SfX7acICk1//L2w1EJfL/2KHd098+3SLC0DG7jS2h0Ikfyyc3M3hZGoK8nQY0uXERFhKcHtyQ8NplZ24rfi7+wZm+ziqquDsy9SGt0r6bUqe7OV3auIDg8a7J65zUdLc9MICgDj/dvjgLP/bw9z9YkmZnK7rBYOuXSkSynRrWr4eHukm+OIKuVUt8KEAhEhGcGt+RYdCLzdhV+vKgl+07xzPTtVmX4g1Zl+PB2flwdWI//Lj5IdAEtx6ZtOs6ZhNRCDx54f++mxCWnM3dn3jkZVeWdP/bh4ebK368pHyOuDspuRpp78VBo9Hm2hJ7jlq7+l9WVDGjlQ0f/WnyzIqRUZ/CKTUxj6SVFVZeqUdWNR69uzvIDUewOi2W/PbRE61yKTisDEwjKQHMfT965KYiNR8/y1bLcJ2o/cuY88SnpBfYsdHERWvh45psjWBMSTVPv6mXSDNEZhrerT2u/mny1PKTAsWNyWn7gNE9N3Ub7hl4XVYaLCGNvaEdiagafLDqY5/bJaRl8t/IwvZvXpUdA4UaR7NmsLq39avLD+tA8i1mW7T/NyoNRPDc0EJ+aJTPAXHH516lOKz/PPOsJ5mw/iQjc3KXhZZ9ZQTuQE2eTsvvd5Gfd4TN8vuRQno9vVoQ4NGvdvN3hpGZkcmtX/3zXu79PU7w83Phy2SGCI+Jp6l293DWQKC1OPWsRqQ1MAIIABR4GDgAzgADgGHCHqlaM0a0K4daujVhzKIrPlx6kTwvvy4an3Wl3JHOk+CHQ15PNx3L/CtMyMtlwJJobO1/+j3qlcnER/ja4Jc9O386CvZEO9bxdc+gMY37cSqCfJz/kUhne0rcmD1wVwPdrjzK6VxOCGl2eE/t1axin41P4752dC51mEeHePk1587c9bDsec9m8FCnpGbw7bx8tfGpwf5+AQu/fmQa18WXi6qPEJ6ddNF+yqjJn+0muauGdZyOKoW19advAi6+Xh3Bzl0a5TvZyPiWd9/8Kdqi56cwtYfz8eO/LBmDMafa2kwT6etK+gNFua3q481DfZny+9BC1qrnTu3n5HCK6NDg7R/A5sEBV2wCdgGDgNWCpqgYCS+33lY6I8N6oDjSpW53nf95+WaufXWExVK/iSgsHWo209PXkZEwSCbmMxbMrLIaElPQKUT+Q03UdGtDcpwZfLsu9l2hOG45E8+gPm2lerwY/PdIrz8rw54YG4l2jCmPn7r1sn6npmfxvxWG6NqnNVS2K1vNzVJdGeFZ148f1xy777Ps1xzgWncjYG9qX2VAeeRnc2pf0TL2sI+S24+cIjU5kVJe877yzivKOnDmf69Dvm46eZcTnq5i+6Thj+jdn/7sjOPKva3N9zHyiD5FxyYyesDHPzp+h0efZmkdRVW4e6htAjSquxCYVbw6CK53TfnEi4gX0ByYCqGqqqsYANwFT7NWmADc7Kw3lnWdVN768uytRCSm8MnPXRRefHfbUlI5Ml9fSrjA+nEvx0JpD0YhQ5ItXeeXqIvxtYEuCI+JYEnx5sUVGprLhSDSvz9nNQ5M241+nOj892ivfdtteHu78/ZrWbA09lz00QZY528M4GZPEM0MCHbrA5Mazqhu3dm3EX7sjL7qQnYpL5stlhxjWzq/A9vploVvTOnh5uF3Wy3jWtpNUc3ctsNPbiPb1aenryVfLDmUX5SWnZfCvv4K5c/x6BOGXMX34x7Vt8XB3xcVFcn10D6jL9w/24MS5RO6dsDHXKTVnb8u7qCo3tatX4f6rAoDS6VFcXjnz1qM5EAVMEpHtIjJBRGoAfqoaAWA/59qlTkQeF5EtIrIlKqrk5hgubzr41+LVEW1YtO9U9vgsqemZBIfHOdxqJNAv7yaka0PO0KFRrQo5w9eNnRvSuG617BElVZWtoed4+4+99PlgKXeN38Csbda0pNMe7eXQxC63d2tMR/9afDA/OHu00/SMTL5efpiO/rUYWMwL9X19mpKakcmMzRfa1384fz/pGcob17Ut1r6dxc3Vhf6tfFh+IOqiC/m8neGMCKpfYLm6i4vw9KCW9jDQkew5GcuNX61h/Koj3NOzCfOfu9rhOpfezb2ZcH8Pjpw5z70TNxKbeGFwO0eKqnLz5MAWPDskkAHlMAiXFmcGAjegK/CtqnYBzlOIYiBVHa+q3VW1u49Pxf4DPdy3GQNb+/Den8HsC49jf2QcqRmZeQ4tcammdavj7iqXVRifT0ln2/FzFaK1UG7cXV14amBLdoXF8sKMHfT7aDm3fruOqRuP07lxbb64uwtb3xjGl3d3ybdMOScXF2HsDe05FZfC18utivy5O8M5fjaRpwfl34vYES19a3JVC2+mbgglPSOTraFnmbP9JI/1b0ZT7xrF2rczDW7jS1R8CnvtZpbL958mLjmdUV0ub6efm+s7NiDAuzpv/r6Xm79eS2xSGlMe7sn7ozpQo5AVtP0C6zH+vm4cOpXA/d9vzJ60aGvoOY6fTbyoP4MjvDzceXFYq3IxplZZcWYgCAPCVDVrluyZWIHhlIg0ALCfK8bsF8Xg4iJ8cnsnaldz55np29hgj4iaX4/inNxcXWhWr8ZlOYJNR8+SnqkVrn4gp1u7+uNfpxrzdkXQys+T/9zeiS1vDGX8/d25sVPDQl9kwCoKuaVrIyasPsqRqAS+Xh5Cm/o1852XujDu79OU8NhklgSfYtzcfdT38uCpgS1LZN/OMqCVDyJktx6ate0kvjWrOnyT4ebqwrNDAomKT+H6jg1Y9PyAYt2BD2ztyzeju7I3PI6HJm0mISXd4aIq43JOazWkqpEickJEWqvqAWAIsM9+PAB8aD//7qw0XEnqeVblszs7M3riRj5ZeBDvGlXwr+N49ralryf77Lu1LGtCzlDVzeWyFioVSRU3F/54uh8ilGjx12sj2rBwTyT3TthIeGwyX93TpcTGoBna1o8GtTx4ddZuYpPS+PyuzkUKWKXJ27MqnRvXZtmB09zbuwkrDpzmkX7NHKrDynJLV3/6FLLYJj9D2/nx5d1deHr6dh6etJngyDhGBNUv999leeTs5gnPAFNFZBfQGfgXVgAYJiKHgGH2ewO4qmU9nhrYgtSMTDr61ypUMURL35ocP5t40Xj9a0PO0COgboXP8tapUaXE60B8vTx4Zkgg4bHJtPCpUaJzBru5unBPzybEJqXRI6AON3a6Mpr2Dmrty66wGKasO0Z6pjIql+EbClJSQSDLyA4N+O+dndkSepb45PSLRho1HOfU0KmqO4DuuXw0xJnHvZK9MLQVkbEpDGtXuGFpA309yVQ4euY8bRt4cTo+mf2R8bw6wvxjFNVDfQM4eCqe27r5F+rO1xGjezdl6/Fz/GNk22LXO5SWwW18+XTxQb5ZcZh2DbxoU0564d7YqSECrDgQxVUtKm4xqDOZPFQ54+bqwn/u6FTo7bIGnzt0OoG2DbxYf9iqZ6jI9QPOVtXNlU/v6OyUfdetUYXJD/V0yr6dpX1DL3xrVuV0fEq5u/O+oVNDbrhCclblUfnquWIUWbN6NXCRC01I1xw6Q+3q7rQroHelYTgqa7IaVxepUD3VDZMjqDA83K2ZlUJOx1807HRJF2kYldvL17Tmlq7++NZ0fIpWo/wzgaACaelbk0OnEjhy5jzhscn8zRQLGSWsnmdVhzrmGVcWUzRUgQT6eXIs+jwr7KkrTf2AYRiOMIGgAmnp40lahjJ903Ea161WrnuqGoZRfphAUIHkHHPI5AYMw3CUCQQVSM4hqyvq+EKGYZQ8EwgqkBpV3WhU2+q5aTrWGIbhKNNqqILp1LgWfl5VqZvPuPuGYRg5mUBQwXx8WyfSCzGPr2EYhgkEFYwZedEwjMIydQSGYRiVnAkEhmEYlVyBgUAs94rIW/b7JiJyZQ2baBiGYeTJkRzBN0Af4G77fTzwtdNSZBiGYZQqR2oWe6lqVxHZDqCq50TEtE00DMOoIBwJBGki4googIj4AJmO7FxEjmHlIDKAdFXtLiJ1gRlAAHAMuENVzxU65YZhGEaJcKRo6AtgDuArIu8Da7DmHnbUIFXtrKpZU1a+BixV1UBgqf3eMAzDKCMF5ghUdaqIbMWaZ1iAm1U1uBjHvAkYaL+eAqwAXi3G/gzDMIxiKDAQ2EU5p4HpOZa5q2qaA/tXYJGIKPCdqo4H/FQ1AkBVI0Qk11naReRx4HGAJk2aOHAowzAMoygcqSPYBjQGzmHlCGoDESJyGnhMVbfms21fVQ23L/aLRWS/owmzg8Z4gO7du5sxEwzDMJzEkTqCBcC1qlpPVb2BkcAvwFNYTUvzpKrh9vNprHqGnsApEWkAYD+fLnryDcMwjOJyJBB0V9WFWW9UdRHQX1U3AHlOXioiNUSkZtZrYDiwB5gLPGCv9gDwexHTbhiGYZQAR4qGzorIq8DP9vs7gXN2k9L8mpH6AXNEJOs401R1gYhsBn4RkUeA48DtRU69YRiGUWyOBIJ7gLHAb1h1BGvsZa7AHXltpKpHgE65LI/GaoFkGIZhlAOONB89AzyTx8chJZscwzAMo7Q50nzUB3gFaA94ZC1X1cFOTJdhGJVYWloaYWFhJCcnl3VSrggeHh74+/vj7u5epO0dKRqaijUkxPXAE1gVvFFFOpphGIYDwsLCqFmzJgEBAdj1jEYeVJXo6GjCwsJo1qxZkfbhSKshb1WdCKSp6kpVfRjoXaSjGYZhOCA5ORlvb28TBBwgInh7excr9+TQoHP2c4SIXAeEA/5FPqJhGIYDTBBwXHG/K0cCwXsiUgt4CfgS8AKeL9ZRDcMwjHLDkaKhc6oaq6p7VHWQqnYDzjo7YYZhGBVJQEAAZ86cKetk5MqRQPClg8sMwzAqJFUlM9OhaViuSHkGAhHpIyIvAT4i8mKOxziszmSGYRgV1rFjx2jbti1PPfUUXbt25ZFHHqF79+60b9+esWPHZq8XEBDA2LFj6dq1Kx06dGD/fmtszejoaIYPH06XLl0YM2YMqhfGzvz0008JCgoiKCiIzz77LPt4bdq04dFHHyUoKIjRo0ezZMkS+vbtS2BgIJs2bXLaueZXR1AF8LTXqZljeRxwm9NSZBiGkcPbf+xlX3hcie6zXUMvxt7QvsD1Dhw4wKRJk/jmm284e/YsdevWJSMjgyFDhrBr1y46duwIQL169di2bRvffPMNn3zyCRMmTODtt9+mX79+vPXWW/z555+MHz8egK1btzJp0iQ2btyIqtKrVy8GDBhAnTp1CAkJ4ddff2X8+PH06NGDadOmsWbNGubOncu//vUvfvvttxL9HrLkGQhUdSWwUkQmq2qoU45uGIZRjjVt2pTeva3W8r/88gvjx48nPT2diIgI9u3blx0IbrnlFgC6devG7NmzAVi1alX26+uuu446deoAsGbNGkaNGkWNGjWyt129ejU33ngjzZo1o0OHDgC0b9+eIUOGICJ06NCBY8eOOe08HWk1VFVExmPNMZy9vulZbBhGaXDkzt1Zsi7WR48e5ZNPPmHz5s3UqVOHBx988KJ2+1WrWgMxu7q6kp6enr08t2adOYuILpW1HwAXF5fs9y4uLhftt6Q5Uln8K7AdeAP4e46HYRhGpRAXF0eNGjWoVasWp06dYv78+QVu079/f6ZOnQrA/PnzOXfuXPby3377jcTERM6fP8+cOXO4+uqrnZr+gjiSI0hX1W+dnhLDMIxyqlOnTnTp0oX27dvTvHlz+vbtW+A2Y8eO5e6776Zr164MGDAge8rdrl278uCDD9KzZ08AHn30Ubp06eLUop+CSH7ZFAC7lVDWDGMpWctVtdT6EnTv3l23bNlSWoczDKOMBQcH07Zt27JOxhUlt+9MRLaqaveCtnUkR5A1m1jO4iAFmjucQsMwDKPccmQ+gqINZ2cYhmFcEQqsLBaR6iLyht1yCBEJFJHrHT2AiLiKyHYRmWe/rysii0XkkP1cp+jJNwzDMIrLkVZDk4BU4Cr7fRjwXiGO8RwQnOP9a8BSVQ0EltrvDcMwjDLiSCBooar/xh6OWlWTsOYuLpCI+APXARNyLL4JmGK/ngLc7GhiDcMwjJLnSCBIFZFqWBXEiEgLcrQeKsBnWNNc5hytyU9VIwDsZ9/cNhSRx0Vki4hsiYoyE6IZhmE4iyOBYCywAGgsIlOxinNeKWgjux7htKpuLUrCVHW8qnZX1e4+Pj5F2YVhGEaRHTt2jKCgoGLtY+7cuXz44YcllCLncaTV0GIR2YY1PaUAz6mqI4Nq9wVuFJFrsSa99xKRn4BTItJAVSNEpAFWHwXDMIwK58Ybb+TGG28s62QUyJFWQ6Owehf/qarzgHQRubmg7VT1H6rqr6oBwF3AMlW9F5jLhb4JDwC/FzXxhmEYzpSens4DDzxAx44due2220hMTOSdd96hR48eBAUF8fjjj2ePHfTFF1/Qrl07OnbsyF133QXA5MmTefrppwE4deoUo0aNolOnTnTq1Il169aV2XldypEOZWNVdU7WG1WNEZGxwG9FPOaHwC8i8ghwHLi9iPsxDKMymP8aRO4u2X3W7wAjCy6yOXDgABMnTqRv3748/PDDfPPNNzz99NO89dZbANx3333MmzePG264gQ8//JCjR49StWpVYmJiLtvXs88+y4ABA5gzZw4ZGRkkJCSU7DkVgyN1BLmt40gAyaaqK1T1evt1tKoOUdVA+9lMe2kYRrnUuHHj7HGF7r33XtasWcPy5cvp1asXHTp0YNmyZezduxeAjh07Mnr0aH766Sfc3C6/RC5btownn3wSsEYprVWrVumdSAEcuaBvEZFPga+xWg49AxSpAtgwDKPQHLhzd5ZLh5EWEZ566im2bNlC48aNGTduXPZw1H/++SerVq1i7ty5vPvuu9kB4krgSI7gGawOZTOAX4Ak4G/OTJRhGEZ5cPz4cdavXw/A9OnT6devH2DNSJaQkMDMmTMByMzM5MSJEwwaNIh///vfxMTEXFb0M2TIEL791hrIOSMjg7i4kp11rTjyzRGIiCvwu6oOLaX0GIZhlBtt27ZlypQpjBkzhsDAQJ588knOnTtHhw4dCAgIoEePHoB1Yb/33nuJjY1FVXnhhReoXbv2Rfv6/PPPefzxx5k4cSKurq58++239OnTpwzO6nKODEM9F7hPVWNLJ0mXM8NQG0blYoahLjxnD0OdDOwWkcXA+ayFqvpsYRNqGIZhlD+OBII/7YdhGIZRATnSs3iKPdZQE1U9UAppMgzDMEqRIz2LbwB2YI03hIh0tusNDMMwjArAkeaj44CeQAyAqu4AzKxlhmEYFYQjgSA9lxZD+Tc1MgzDMK4YjgSCPSJyD+BqT1P5JVB+RksyDMMo5wYOHEhWE/hrr70217GIypKjPYvbY01GMw2IBZ53YpoMwzAqrL/++uuyzmZlLc9AICIeIvI88G+sUUL7qGoPVX1DVZNLK4GGYRhl4dixY7Rp04ZHH32UoKAgRo8ezZIlS+jbty+BgYFs2rSJ8+fP8/DDD9OjRw+6dOnC779bo+onJSVx11130bFjR+68806SkpKy9xsQEMCZM2cum/jmk08+Ydy4cYCVg3jhhRfo378/bdu2ZfPmzdxyyy0EBgbyxhtvlPi55td8dArWPMWrgZFAW0xOwDCMUvbRpo/Yf3Z/ie6zTd02vNrz1QLXCwkJ4ddff2X8+PH06NGDadOmsWbNGubOncu//vUv2rVrx+DBg/n++++JiYmhZ8+eDB06lO+++47q1auza9cudu3aRdeuXQudxipVqrBq1So+//xzbrrpJrZu3UrdunVp0aIFL7zwAt7e3kU59VzlFwjaqWoHABGZCGwqsaMahmFcAZo1a0aHDh0AaN++PUOGDEFE6NChA8eOHSMsLIy5c+fyySefAJCcnMzx48dZtWoVzz5rDb7QsWNHOnbsWOhjZ81s1qFDB9q3b0+DBg0AaN68OSdOnCi1QJCW9UJV0y8djtUwDKM0OHLn7ixVq1bNfu3i4pL93sXFhfT0dFxdXZk1axatW7e+bNuCrplubm5kZmZmv88azvrSY+c8bs5jl6T8Kos7iUic/YgHOma9FpHyM36qYRhGGbnmmmv48ssvs6er3L59OwD9+/dn6tSpAOzZs4ddu3Zdtq2fnx+nT58mOjqalJQU5s2bV3oJv0SegUBVXVXVy37UVFW3HK+9CtqxXdm8SUR2isheEXnbXl5XRBaLyCH7uU5JnpBhGEZpefPNN0lLS6Njx44EBQXx5ptvAvDkk0+SkJBAx44d+fe//03Pnj0v29bd3Z233nqLXr16cf3119OmTZvSTn62AoehLvKOrXxRDVVNEBF3YA3wHHALcFZVPxSR14A6qppv3s8MQ20YlYsZhrrwijMMtSP9CIpELVlT9LjbDwVuwmqRhP18s7PSYBiGYRTMaYEArBnORGQHcBpYrKobAT9VjQCwn33z2PZxEdkiIluioqKcmUzDMIxKzamBQFUzVLUz4A/0FJGgAjbJue14Ve2uqt19fHyclkbDMMonZxVbV0TF/a4cGYb6FrtiN7aorYZUNQZYAYwATolIA3vfDbByC4ZhGNk8PDyIjo42wcABqkp0dDQeHh5F3ocjM5T9G7hBVYMLs2MR8QHSVDXGnthmKPARMBd4APjQfv69cEk2DKOi8/f3JywsDFMs7BgPDw/8/f2LvL0jgeBUYYOArQEwRURcsXIev6jqPBFZD/wiIo9gjWF0exH2bRhGBebu7k6zZmbak9LiSCDYIiIzgN+wRiAFQFVn57eRqu4CuuSyPBoYUrhkGoZhGM7iSCDwAhKB4TmWKZBvIDAMwzCuDI5MXv9QaSTEMAzDKBt5BgIReUVV/23PSHZZ1b2qPuvUlBmGYRilIr8cQVYFsRnbwTAMowLLLxC0EJEewFRVLdkxTw3DMIxyI79A4A98DrQRkV1YE9avBdar6tnSSJxhGIbhfHkGAlV9GUBEqgDdgauAh4H/E5EYVW1XOkk0DMMwnMmR5qPVsJqQ1rIf4cBuZybKMAzDKD35tRoaD7QH4oGNWEVDn6rquVJKm2EYhlEK8ht0rglQFYgETgJhQEwppMkwDMMoRfnVEYywZxlrj1U/8BIQJCJnsSqMx5ZSGg3DMAwnyreOQK0xYPeISAwQaz+uB3oCJhAYhmFUAPnVETyLlRPoC6RhNx0FvsdUFhuGYVQY+eUIAoCZwAtZU0sahmEYFU9+dQQvlmZCDMMwjLLh1DmLDcMwjPLPBALDMIxKzmmBQEQai8hyEQkWkb0i8py9vK6ILBaRQ/ZzHWelwTAMwyiYM3ME6cBLqtoW6A38TUTaAa8BS1U1EFhqvzcMwzDKiNMCgapGqOo2+3U81vwGjYCbgCn2alOAm52VBsMwDKNgpVJHICIBWBPZbwT8spqj2s++eWzzuIhsEZEtUVFRpZFMwzCMSsnpgUBEPIFZwPOqGufodqo6XlW7q2p3Hx8f5yXQMAyjknNqIBARd6wgMFVVZ9uLT4lIA/vzBsBpZ6bBMAzDyJ8zWw0JMBEIVtVPc3w0F3jAfv0A8Luz0mAYhmEUzJGJaYqqL3AfsFtEdtjL/gl8CPwiIo8Ax4HbnZgGwzAMowBOCwSqugaQPD4e4qzjGoZhGIVjehYbhmFUciYQGIZhVHImEBiGYVRyJhAYhmFUciYQGIZhVHImEBiGYVRyJhAYhmFUciYQGIZhVHImEBiGYVRyJhAYhmFUciYQGIZhVHImEBiGYVRyJhAYhmFUciYQGIZhVHImEBiGYVRyJhAYhmFUciYQGIZhVHLOnLP4exE5LSJ7ciyrKyKLReSQ/VzHWcc3DMMwHOPMHMFkYMQly14DlqpqILDUfm8YhmGUIacFAlVdBZy9ZPFNwBT79RTgZmcd3zAMw3BMadcR+KlqBID97JvXiiLyuIhsEZEtUVFRpZZAwzCMyqbcVhar6nhV7a6q3X18fMo6OYV2Iv4EqlrWyTAMwyhQaQeCUyLSAMB+Pl3Kxy8VB88d5LrZ1/HHkT/KOimGYRgFKu1AMBd4wH79APB7KR+/VCw8thBF+evoX2WdFMMwjAI5s/nodGA90FpEwkTkEeBDYJiIHAKG2e8rnCWhSwDYGLGRuNS4Mk6NYRhG/pzZauhuVW2gqu6q6q+qE1U1WlWHqGqg/Xxpq6Ir3pGYIxyJPcINzW8gPTOdlSdWlnWSDMMw8lVuK4uvVItDFwPwXNfn8Kvul/3eMAyjvDKBoIQtDl1MZ5/O+NXwY1jTYaw9uZbzaefLOlmG4XTpmemM+n0UX+/4uqyTUn5s/wne84OfR8Oe2ZBaPq8FJhCUoBNxJzhw7gBDmw4FYGjToaRmprI6bHUZp8wwnG9l2EpCYkKYGjyVpPSksk5O2UtPheX/ghq+ELYFZj4EH7eEmQ9D8DxISy7rFGYzgaAELT5uFQMNbTIE9s6hc61AvD28TfFQQVTh4CJIiinrlBjF8PP+n6nmVo341Hj+OmJazLFzGsSdhBv+Cy/ugwfmQae74MgKmDEaPgmEOU/A6eCyTqkJBCVpSegS2nm3o9HxLfDrg7iu/g9Dmgxh9cnV5g4pPzumwbTbYe4zZZ0So4iOxh5lQ8QGHu3wKIF1Avn5wM+Vu0NlRhqs/hQadoUWQ8DFFZpdDdf/F146CPfOhrY3wv4/YdodkFa21wcTCEpIREIEu8/sZliTYbDqE2vh5gkMrd+LpPQk1p1cV7YJLK+iDsJfL4NHLQiea90tGVecGQdm4Obixi2Bt3BX67vYf3Y/O6N2lnWyys7uXyEmFAa8AiIXf+bqBi2HwM1fw11TIeY4rPuqbNJpM4GghCw5bvUdGJZZBU7thquegdQEuh/dRO2qtbOLjQqUmWFlF7/qketj7zfd+Nvk7pwK3+rEsyklaclWeambBzy+Emo3hfmvWXdTxhUjMS2R30N+Z3jT4dSrVo/rm1+Pp7sn0/dPL+uklY3MDFj9H6jfAVpdOgDzJZr1h3Y3W+vHhpVK8nJjAkEJWRK6hMDagTTdNAlqN4EhY6HN9bhvHM+ghn1ZeWIlqRmpBe9o+4+wc7q1D7/2Fz3ifFvzkieskhQ+XfjElX/BXPyWFTRH/Q/qNoMRH0BUMGyeWNYpMwph3pF5JKQlcHebuwGo7l6dm1rexKLQRZxJOlPGqSsDe+dAdAj0//vluYHcDH8XUFj0ptOTlpfKHQjiIqya/WI6k3SG7ae3M6xmcwjfBv1eBFd364eQEsvQxCQS0hLYELEh/x0lxcDSd6BJHxg9E26fnP3Q2ybxtq8fkWQwtHZb/nJJZtuCF4ud9hKjCtGHrWdH7P8LNn0HvZ+CVtdYy1pfC80HWS0tzpf/C0hGZgZh8WVwF6cKZ4/mWa6sqoQnhJdSUpSfD/xMm7pt6OTTKXv5na3vJD0zndmHZpdKOpwqORbiTzm2bmamVTTs0wba3ODYNrWbQL8XYO9sOLam6OkshsobCCL3wBedYcoNkJJQrF0tDV2Kogw7ug28GkHne6wPGnaGwGvovXMunu41soeeyNOKDyHpHIz892V3ErMOzWJR6CKe7vI07187GT+pyocnF5ERsrRYaS8x67+GL7vC9LsK/qeJPQm/PwX1O8LQcReWi8DIjyDtvBUQy7G0zDReXvkyI2ePZNbBWaVz0KgDsPwDq5jwi87wTW8IXX/Zap9v+5xrZl3DT/t+cnqStp3exqFzh7ir9V1Ijt9ss1rN6N2gN78c+IX0zHSnp8Np9v8JX3azvvPwHQ6s/4eVq736ZXApxOW173NQqwnMfxUySv/7qpyBIPW81abXvRqEbYZpd0JqYpF3tzh0MQHV/GhxYiv0fR7cql74cMArVEk6y4Cq9Vl2YhlpmXkU55wOhk3joduD0KDjRR8djjnMR5s+oneD3jwc9DDV3avzcp83CK5ahdkL/gYJZTxfw8ltsGScVSZ6ZIV1gdo7J/d1MzNg9mNWTuy2SRd/VwA+raHnGNj2g2P/eGUgPTOd11e/zpLjS2heqzlvr3+b30OcNH7i2SPWHeY3V8HXPWHlR1CzPgx7x8oZTBoJi97IbpO+LnwdE/dMpK5HXT7a/BE/7//ZOemy/bz/Z2pWqcm1za+97LO72tzFqcRTV+YwK8mxMOdJ+Pke6/v2qAU/3gyRu/PeRhVWfQx1W0DQLYU7nns1q4jo1B7YNrk4KS+SyhkI5r/K+egQvupxO9uGvQ7H18HPdxepg8e55HNsObWFYUnJiKcfdL3v4hX8u0PzQQwL20dsSixbIrdcvhNV606gak0Y9MZFHyWnJ/Pyypep7l6df/X7Fy5i/cmuaXkT3eq05YvqrsTOeczKkhYk/pR1wd46GRJLaJin5DirwtfTD+6fC2NWQZ0A+PVBmPnI5cdZ9TGEroXrP4V6LQFIyUjhi21fsDlys7XOwFehRj2Y/4rjRU2lJCMzg7fWvsX8Y/N5sduL/HLDL/Rq0Iu31r1Vsm3nQ5bC+IHwRRdY9i5U9bRyii/thwfnWXeQT66zbhzWfQnjB3Lm6Er+ufqftKjVgj9G/cEA/wG8v/H9i3Msp4Otorftxc8tRCVGsSR0CTe3vJlqbtUu+3yA/wDq16jP9APlu9L41PlTfLDxAxYcW0BiWqJ9M3MV7JphFe8+ugwemAvu1eGHm/Ju939wgRUorn7Jai5aWO1ugoCrYdl7Jff/6ShVLfePbt26aYnZPVM3feCj1/zYU4MmB2n3H7vrptUfqI6tpfrjrappyYXa3ayDszRocpDufd9bde2Xua90dI0mjaulPaZ01nfWvXP553t/Vx3rpbrhu8s+enf9uxo0OUhXh62+7LP90fu14+QO+q/Pm6qu/SL/hO6Zo/phgHWcsV6qb9dV/ek21e3TVJNiCj7R3GRmqs58VHVcbdVj6y4sT09TXfGRdYyPW6keXGQtP7rGWnfW49mrpqSn6JOLn9SgyUEaNDlIP9z4oSalJalunWKlc+eMoqXNCTIyM/SttW9p0OQg/d+O/2UvT0xL1IcWPKSdpnTShUcXFv9ABxervlNP9fMuqms+Vz13vMD1Mz5prWO+bandpnTSA1F7VdX6bscsHqMdJnfQ3+eNUf2q14W//1gv1fXfFCuZ3+z4RoMmB+mx2GN5rjN+53gNmhykh2MOF+tYznL6/Gm9fvb12b+/HpM76d+/aqZLvumsyTl/06qqZ0Ks3/O/W6pGHbz4s8xM1fGDVP8bpJqeWvQERe5RHVdHdd6LRd9HDsAWdeAaW6lyBMlnDvDRytd4uIEfrtW9+XLwlzT0bMjfQmezY8grELIYfnmgUBXIi0MX0wh32rp5QfeHcl8poC8eTa7i6uRUlh5fSkZmxoXP0pJg0evg2w66P3zZvmccmMGD7R+kX6N+l+22dd3W3N76DmbUqsmhle/DyVyalCadg1mPwq8PQJ2m8LdN8PgKq5L2dDD89gR8HGiPhTKrcGOh7JwOu3+Bgf+Apn3IVDtX4upmtZ9+dClUqwNTb7M6i81+zMotXGf1s8gqZ199cjWv9XyNu9vczU/BP3H7H7ez278TNOxitaRIiXc8TU6iqry/4X1mH5rNmI5jGNNpTPZn1dyq8dXgr+jo05FXV73KsuPLin6grF6nPq3hsaXQ91mo3Tj/bQKH8sPgZ1lbzYNXoqJoNfspOLqaKuu/4bPDe+mZlMSbUWv4q3oVuPYTeGEftLkeFrwGmycUKZlpmWnMPDCTvg370tSraZ7r3RJ4C+4u7szYP6NIx3Gm6KRoHlv0GKcSTzG562tMTHDl+rgY1teszfPV0xm49kVeX/M6q8NWW0W63i2snAFq1S1GH76ws8PLrP+/rIYiDsq6EGfzaw89HoUt3+dfDFXSHIkWZf0oiRzB7sjtesOkTho0OUjfW/Gqnk89r6rWHcF1s6/T3lN7664V71h3Sj/fa93VFiA2JVY7T+mkn3zWRHXVf/JfOWSZzv93fQ2aHKSbIzZfWL7iI+uYR1ZetPrJ+JPaZ1ofveuPuzQ1nzuMc0nn9KqpffSR/2uvmZ91UE2KvfDhwcWqn7S27sxXfHT5nUpmpurxjap/vWrd6Yz1Uv2gierOX6zP8hN1UPW9+qqTrtOU1ET9dMun2uOnHvr19q81NSPHcVKTVBe+YeW43vZWPblNVVXTMtL0heUvaNDkIJ0WPC179fXh63Xor0O105RO+sWK1zR1rJfq4rH5p8XJMjMz9YONH2jQ5CD9dMunmpnHdxOfEq/3zLtHO//QWVeeWJnrOvk6ukb1XT/Vr/uoJpxxeLPdUbu185TO+sLyFzRz9+yLc37jB2ni6k/1gT/u0k5TOumiY3buLC1Fdeod1jpbpxQ6qQuPLtSgyUG6/PjyAtd9ddWr2ntq7+z/ufLgXNI5HTXnRu0+pbNumjTE+n1+GqR6ZKWmZqTq6rDV+vrq17XP1D4aNDlI+07vq2PXjtUN4Rs0PWK39R3/p53q2WPW/8rEa1T/07ZQJQpbIrfotbOu1Xvm3XNxjinxrLX/768t+P+wADiYIyjzi7wjj+IEgtT0VP1y25faaXIHHTKhja5b98ll60QkROiImSO0z7Q+unfZOOuf49eHVDPS89333JC5GjQ5SHd80uziC3BuMjM14f8GaddJQfrB+vetZeeOW//4M+6/aNW0jDS976/7tNfUXno8toBiAVWdHjxdgyYH6aJ/N1Cd+Yhqcrzq3Oes8/iqp+rJ7QXuQzPSVY+sUv2/odZ2M+7L+2KUlqz6bV/VDwM0OHSVjvp9lAZNDtI7/7hTgyYH6R1/3KEh50Iu3iZsi+pRq3grPSNd/77y7xo0OUin7Ln8IhSXEqf/XP1PDZocpLf/0EsPflDfypaXgczMTP1k8yfZxVZ5BYEssSmxevvc27XrD1117cm1jh8odIPqew1Uv+yhGn/a4c3iU+J1xMwROuzXYRqTbBfxxUWqbvtJNfpI9noJqQl675/3aucpnXVZ6DJrYVqy6g+jrIvgjumOp1VVH1rwkA7/dbimF/A/oqq6/dR2DZocpDP2l4NivsRzGrNpgt4+uZt2ndRe133go/pFN9XlH+T6P5ySnqLLQpfpKytf0R4/9dCgyUE64OcB+v7SF3XrJ800479B1nc31kt1w/9yOeDlktOT9ZPNn2iHyR30mpnXaN/pfbXbj930h70/aEZmhrXS5onWPnfPKtbpOhoIRLV8Vcblpnv37rplSy6VrAU4dO4Qr695neCzwdwYn8CrjUfiddM3ua4bnhDOgwseJDE9kYk+g2m9+nPocLvVOsOrYa7bPDv/IfaGb2Bx8/txGfSPghN0cBHPLnmSfbX9WHT3GlxmPgwHFsDTm6y2xEB8ajxf7/iaqcFT+ejqj3JtjXGp9Mx07px3J/FxJ/n98H6qVfOGxGird/Og18Hdo8B9qCo7o3YSFnecfmF7qL3qU6tY58YvofUlvSPnv0r6xv/xff/H+PbkUmpXrc3bV71Nf//+LA5dzLvr3+V82nme7fos97a9F9ccFWeZmsmba99k7uG5PN/1eR7p8EieaVp2fBlvrxtLfPI5nnDx5tZr/w/veq0KPJeSci75HBN2T+CHfT9wZ+s7eb3X6xeaSKpC5C44tQ9aDLJalthikmN4ZNEjHI87zphOY/BwLeD7jzkBWyZAFU/oNQaqegFQx6MO/f37U7NKzVw3U1VeXf0qi44tYtKISXTx7ZLvYeJT4xmzeAzBZ4MZ12ccI5uNpEpmhjXWzbE1cMv/QYfbCvxeQs6FMGruqMv/fsmxcGgxnL+4FZuqcmfor6RpJrNb3o8EDsv+vZcIVWt0z5NbgTyuZ5kZELqW+MNLedy3NgeqVOWLen3p1+0pq7WbAx2/ktKTWB22mgXHFrAqbBUpGSn4ZWRyTUICIzOq0v5vO5Aq1fPdx77ofby+5nVCYkK4o9UdvNT9Jc6nnWfc+nGsCltFj/o9eLfvuzSqXh/GD4DEc9b1oUqNInwxICJbVbV7geuVRSAQkRHA54ArMEFV852ysqiBYNy6cSw/vpS3Tp1miFsdeGwZ5POHOhF/ggcXPEh6Zjrfe/ejxZqvALE6eAXdYnUF9/QBrG71/af15raEJF57eJN10SyIKn9M6M0/qyQytdNLdPztORj4DxL7PsvKsJXMPzqfNSfXkJaZxq2BtzLuqnEOn+vmyM08vPBhnpK6PHkuFm7+BppeVUBylOCzwSw4uoCFxxYSft7qhOQmbvTybs/IkwcYHHGImp1HwzUfgIcXHJjP0Zn38kZAO3alxzAiYASv93qd2h61s/d7JukM76x/h+UnltPNrxvv9X0P/5r+ZGom76x/h1mHZvFU56d4stOTBZ7X2eSzvPfnwyxOOIyLKr2kOiMa9WdI96epVTvA4e/HUfGp8Sw7voz5x+azMXwj6ZrOba1u483eb1ottk7vt+pS9s62eo8CIBDQz/qNtL0JanhzNvksjy96nAPnDhQrPe4u7vRr1I+RzUYywH8A1d0v/H7nHJrDW+ve4pkuz/B4x8cd2l9sSixjFo9hb/RePN09GdxkMCP8B9J7+ae4H98It0+yWq/k470N7zHn0ByW3L6EOi5VrNYye2ZbQSAjJddtZnvWYKyPN99HnKJHcgr494CgW63/Ka8GDn8f2bIC8Z5ZVlPlmOMFbnLeqyFP1PdhT3oc/x34XwY2GVT442btK+08y08sZ2HwDNZEbSddBH9Pf0Y0G8GIgBG0qtPqon4VaZlpTNg9gfE7x1PXoy5v9337ono/VeW3kN/4cNOHiAiv9HiFUe71kcnXwp0/QlsHO6ddotwGAhFxBQ5izVkcBmwG7lbVfXltU9RAkJAcR8ovo/E+vgkeWw5+7Qrc5ljsMR5a+BCqyqTe79Ds2EbrxxYVDOJijQ3S/hYWaBx/3zueST4D6X7tlw6nKW73LwzY+g53nE+hh3qwoP1QVp5cQ3JGMr7VfBkeMJyRzUbSoV6Hi35Ijnh55cusOLGCuTf9TsOajfJcL+RcCPOPzWfhsYWExoXiJm70adiHEc1GEOAVwNLjS1lwdAHh58Nxx4V+iecZmelB/77/4LfVb/NZzapUrVqLN3q/wYhmuY+loqrMPTyXDzd9SKZm8vcef2f/2f3MODCDxzo8xjNdninU+R0Mmc+CXZNYGBPMcVdwU+Uql5qMaDyEQd3/hmfNIlxMbIlpiaw4sYIFxxZkB+JGno24JuAaRgSMoI26I/vmwJ45cHovFy78t0KDTnBwofUbiT4E4grNB0LQrWS0GkGCm1veBz4dbHXAq+IJ98yAWv4XfXw09igLjy1k4bGFRCVF4eHqwYDGAxgRMAL/mv7cP/9+OtTrwPhh4y/KdRUkPTOdTRGbmH9sPktDlxKfFk/tKrUYkpzKyKgTdL9xAq5trst124TUBIb8OoShtVrzfpKrFQTSEsGzPrQfZQXDeoGXbZeUnszQebfS27sD/6nW2gocp3Zb32XTvvaN1k1Ws+H8XBqIXdys3uhBt1ijfLpVyXWzxPQknlrzT3ZE7eDjAR8zrOkwh7+vgsQmxbAsbDkLjy1kQ8QGMjSDZrWaMTJgJNc0uwYU/rnmn+yN3su1za7ln73+Sa2qtXLd18mEk7y59k02R25mgP8AxrV9mHoNuxY5beU5EPQBxqnqNfb7fwCo6gd5bVPUQMCaz2DJWGvo10ta5OTncMxhHl74MGkZafhW97UWZqRYbeZT4iAjlWhXV1wQlt2xHFdPX8fTlJnJE5O6stbNajlU16Muw5oOY0TACLr6dc3uJ1AUEQkR3PjbjdRwr0HtqrVzXScpPYnw8+G4iAs96vdgZMBIhjQZctEdPVgX8t1ndjP/6HwWHZ7H6dQYXFXJEOFqn268PfBjfKr7OJSmN9e9ycaIjQA82P5BXuz2YqGDXHa6MjPZd/A3Fu75kQVxh4hwFaqo4p/pQtH2CCddlGQB30zhmnRXRqa7EpTpgiCQngLnjlorNu5t38XeBDX9LkmYWp2B9syyLnIxoeBaBeo0y7vYIfak1Xfkob+ssZbykJGZwbbT21h4bCGLQxdzNtlqY16nah1m3jjzwm+0CFIzUlkXvo4Fxxaw/PgyEtOTqJORQV1yD2BJAuEuyrSTkXRwq2nd0QfdYuWaCwhGH2/+mKnBUwnwCrBPLMVqEZYcZ+ckxPrO8qSQkWqtV6W6VYRWtaYVfAsQnxrPmeQzfHj1h4xsNrLA9YvqbPJZloQuYcGxBWyJ3IKiuIgLXlW8eLP3mwwPGF7gPjI1k2nB0/hs22d4uHnw34H/pUf9HkVKT3kOBLcBI1T1Ufv9fUAvVX36kvUeBx4HaNKkSbfQ0NDCH2zXr3BkOdz0tWODP+UQci6EiXsmkpJbVjcpBuLCGezXg+sHvlv4ZAXP5M8DvzKwx/P0aNADN5d87hoLaenxpfx55M88P3cVV7r6dWVY02HUq1bA3ZctUzPZdnIdyzd9RivfLtzY95+FupBnaiYzD84kMS2RB9o/UOQgcNl+M9LZFfwLC4N/5lTyuSLvx0fcGe5amy4uNXC5LG1idQpsP+qyO/Y8qVq9rffOhtgTea/nXt3qsOTdwuG0pmemsylyE8uPL2d4wPAiXyByk5SexOojC1i57VuS8hkfv1mV2jzT/QUI6G81FXbQ6cTTfLr109wHX0yOsyZxyW+4FwGq17Pq7C7tke6A65pdx5CmQwq9XVGdTjzN4tDFhCeE81DQQw7/v2U5EnuEjzZ9xDtXvYNfDb+CN8hFeQ4EtwPXXBIIeqpqnrOSFDlHYBiGUYk5GgjKokNZGJCzh4w/UDpDJRqGYRiXKYtAsBkIFJFmIlIFuAuYWwbpMAzDMCCPGiEnUtV0EXkaWIjVfPR7Vd1b2ukwDMMwLKUeCABU9S+gBIdqNAzDMIqqUg06ZxiGYVzOBALDMIxKzgQCwzCMSs4EAsMwjEruihh9VESigCJ0LQagHnCmBJNzJTDnXDmYc64cinPOTVW1wLFgrohAUBwissWRnnUViTnnysGcc+VQGudsioYMwzAqORMIDMMwKrnKEAjGl3UCyoA558rBnHPl4PRzrvB1BIZhGEb+KkOOwDAMw8iHCQSGYRiVXIUOBCIyQkQOiEiIiLxW1ukpDBFpLCLLRSRYRPaKyHP28roislhEDtnPdXJs8w/7XA+IyDU5lncTkd32Z1+IPUWYiFQVkRn28o0iElDqJ3oJEXEVke0iMs9+X6HPF0BEaovITBHZb/+9+1Tk8xaRF+zf9B4RmS4iHhXxfEXkexE5LSJ7ciwrlfMUkQfsYxwSkQcKTKyqVsgH1hDXh4HmQBVgJ9CurNNViPQ3ALrar2sCB4F2wL+B1+zlrwEf2a/b2edYFWhmn7ur/dkmoA/WZH/zgZH28qeA/9mv7wJmlIPzfhGYBsyz31fo87XTMgV41H5dBahdUc8baAQcBarZ738BHqyI5wv0B7oCe3Isc/p5AnWBI/ZzHft1nXzTWtb/BE78I/QBFuZ4/w/gH2WdrmKcz+/AMOAA0MBe1gA4kNv5Yc330MdeZ3+O5XcD3+Vcx37thtV7UcrwHP2BpcBgLgSCCnu+djq8sC6McsnyCnneWIHghH2RcgPmAcMr8PkGcHEgcPp55lzH/uw74O780lmRi4ayfnBZwuxlVxw7y9cF2Aj4qWoEgP3sa6+W1/k2sl9fuvyibVQ1HYgFvJ1yEo75DHgFyMyxrCKfL1g51ihgkl0kNkFEalBBz1tVTwKfAMeBCCBWVRdRQc83F6VxnoW+9lXkQCC5LLvi2sqKiCcwC3heVePyWzWXZZrP8vy2KXUicj1wWlW3OrpJLsuumPPNwQ2r+OBbVe0CnMcqMsjLFX3edpn4TVjFHw2BGiJyb36b5LLsijnfQijJ8yz0+VfkQBAGNM7x3h8IL6O0FImIuGMFgamqOttefEpEGtifNwBO28vzOt8w+/Wlyy/aRkTcgFrA2ZI/E4f0BW4UkWPAz8BgEfmJinu+WcKAMFXdaL+fiRUYKup5DwWOqmqUqqYBs4GrqLjne6nSOM9CX/sqciDYDASKSDMRqYJVmTK3jNPkMLtlwEQgWFU/zfHRXCCrFcADWHUHWcvvslsSNAMCgU129jNeRHrb+7z/km2y9nUbsEztQsXSpqr/UFV/VQ3A+lstU9V7qaDnm0VVI4ETItLaXjQE2EfFPe/jQG8RqW6ncwgQTMU930uVxnkuBIaLSB07BzbcXpa3sqhAKcWKmmuxWtscBl4v6/QUMu39sLJzu4Ad9uNarDLApcAh+7lujm1et8/1AHbLAnt5d2CP/dlXXOhR7gH8CoRgtUxoXtbnbadrIBcqiyvD+XYGtth/69+wWnpU2PMG3gb222n9EaulTIU7X2A6Vj1IGtZd+iOldZ7Aw/byEOChgtJqhpgwDMOo5Cpy0ZBhGIbhABMIDMMwKjkTCAzDMCo5EwgMwzAqORMIDMMwKjkTCAynEBEVkf/keP+yiIxzwnGmi8guEXkhx7LXRWSH/cjI8fpZB/c5QUTaFbDOEyJyf3HTb+9rhT3iZFY6ZxawfncR+aIEjjtZRG4r7n6MK59pPmo4hYgkY7Wh7qGqZ0TkZcBTVceV4DHqAxtVtWk+6ySoquclywTrt5+Zx2alSkRWAC+r6pZSPu5krP4a+QYeo+IzOQLDWdKx5lp94dIPRKSpiCy17+SXikiT/HYk1nj1k+wx2beLyCD7o0WAr30XfXUB+wgQa6z/b4BtQGMR+VZEtog1Nv7bOdZdISLd7dcJIvK+iOwUkQ0i4mcvH2cHt6z1PxKRTSJyMCstdu/ZX+zznCHWmPHdHf0C7Tv2/4nIanu/19vLB8qF+RoG5MhJbBeRmmL5WKzx/neLyJ32uiIiX4nIPhH5kwsDnmWNeb9SRLaKyEK5MAzCs/b6u0TkZ0fTblxZTCAwnOlrYLSI1Lpk+VfAD6raEZgKFFTM8TcAVe2ANcTuFBHxAG4EDqtqZ1Vd7UB6WtvH7aKqoVi9zbsDHYEBItIxl21qABtUtROwCngsj327qWpP4HlgrL3sKeCcfZ7vAt3ySdvUHBf0j3MsDwAGANcB/7PPO6eXgb+pamfgaiAJuAWrt3InrLF9PrYv7KPs76CDfR5XQfaYVl8Ct6lqN+B74H17/68BXexzeCKf9BtXMLeyToBRcalqnIj8ADyLdYHK0gfrYgXWEAP/LmBX/bAuVKjqfhEJBVoB+Y3GmptQVd2Q4/0dIvI41v9BA6zJQXZdsk0q1pj5AFux5oTIzewc6wTkSPfndrr3iMil+85pdB5FQ7/YRViHROQI0OaSz9cCn4rIVGC2qoaJSD9guqpmYA1ythLogTVRStbycBFZZu+jNRAELLZKzXDFKtYD6/uYKiK/YQ1/YVRAJkdgONtnWGOs1MhnnYIqqnIbVrcozmfv0BrY62VgiH23+yfW2C2XStMLFWkZ5H3zlJLLOiWR7ku/m4veq+qHwKNANWCDiLQp4Li5fdcC7LVzVp1VtYOqDrc/uw4rZ9cN2CrWKJdGBWMCgeFUqnoWazrCR3IsXoc1wijAaGBNAbtZZa+HiLQCmmANzFUcXliBIdYu9x9ZzP3lZg1wB4DdCqlDEfZxu4i4iEgLrElsLjpvEWmhqrtV9SOsgevaYH1fd4o1/7MPVk5gk738Lnt5AyCrruUA4CMifex9uotIexFxARqr6nKsCYNqAxdVvBsVg4nuRmn4D/B0jvfPAt+LyN+xZud6CKwmmQCq+r9Ltv8Gq3x8N1Yl9IOqmmIXYxSJqu4Uke3AXqw5XdcWeWd5+warPmMXsB2rmCU2j3WnikhW8dkZVR1qvz4ArAT8gCdUNfmS837erjzPwBq+ej5WcVYfrDlwFXhFVSNFZA7WNKC7sUblXQmgqqliNSP9wq7PccPKyR0EfrKXCfBfVY0pxvdhlFOm+ahhOImIuALu9sW7Bdaww61UNdXB7SdjmncapcDkCAzDeaoDy+1WOQI86WgQMIzSZHIEhmEYlZypLDYMw6jkTCAwDMOo5EwgMAzDqORMIDAMw6jkTCAwDMOo5P4fHeomYzxxr18AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  100001  episodes done in 6166.19 seconds\n"
     ]
    }
   ],
   "source": [
    "#double DQN, upgrade=False\n",
    "Q_network_d, Q_target_d, info_d = gen_train(double=True, upgrade=False, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6ee22e16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with the warmup\n",
      "Training at episode 0:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9899901  loss: tensor(0.4130)\n",
      "average rewards unscaled in previous 10 games: 100.0\n",
      "300 games greedy evaluation: 82 win random, 0 win basic, 1 win medium\n",
      "Training at episode 2500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.9777312930276878  loss: tensor(3.4964)\n",
      "average rewards unscaled in previous 10 games: -50.0\n",
      "300 games greedy evaluation: 66 win random, 3 win basic, 3 win medium\n",
      "Training at episode 5000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9657401663311417  loss: tensor(1.8497)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 75 win random, 6 win basic, 4 win medium\n",
      "Training at episode 7500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.953600438195009  loss: tensor(1.9383)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 68 win random, 2 win basic, 4 win medium\n",
      "Training at episode 10000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9418110716942316  loss: tensor(1.4062)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 64 win random, 3 win basic, 2 win medium\n",
      "Training at episode 12500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.929897746383275  loss: tensor(0.6130)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 65 win random, 6 win basic, 1 win medium\n",
      "Training at episode 15000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.9184014163678317  loss: tensor(1.6268)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 69 win random, 2 win basic, 1 win medium\n",
      "Training at episode 17500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.9071832834995213  loss: tensor(0.9938)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 72 win random, 2 win basic, 0 win medium\n",
      "Training at episode 20000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8959946520615043  loss: tensor(1.6116)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 70 win random, 8 win basic, 4 win medium\n",
      "Training at episode 22500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8849882627365508  loss: tensor(0.3526)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 69 win random, 5 win basic, 1 win medium\n",
      "Training at episode 25000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8741170757877403  loss: tensor(1.6477)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 71 win random, 0 win basic, 3 win medium\n",
      "Training at episode 27500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.8627235080258032  loss: tensor(2.9472)\n",
      "average rewards unscaled in previous 10 games: 30.0\n",
      "300 games greedy evaluation: 61 win random, 0 win basic, 0 win medium\n",
      "Training at episode 30000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8518531830991531  loss: tensor(0.9335)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 60 win random, 2 win basic, 4 win medium\n",
      "Training at episode 32500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8418266653948698  loss: tensor(1.0939)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 56 win random, 0 win basic, 1 win medium\n",
      "Training at episode 35000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8305632334134673  loss: tensor(2.7678)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 69 win random, 4 win basic, 1 win medium\n",
      "Training at episode 37500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8205082819169723  loss: tensor(2.4817)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 67 win random, 3 win basic, 4 win medium\n",
      "Training at episode 40000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.81109399418897  loss: tensor(2.0442)\n",
      "average rewards unscaled in previous 10 games: 30.0\n",
      "300 games greedy evaluation: 61 win random, 0 win basic, 0 win medium\n",
      "Training at episode 42500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.8011064900189495  loss: tensor(2.4566)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 71 win random, 0 win basic, 0 win medium\n",
      "Training at episode 45000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7899374889130599  loss: tensor(1.6437)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 66 win random, 6 win basic, 1 win medium\n",
      "Training at episode 47500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7813895116123465  loss: tensor(2.4598)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 62 win random, 1 win basic, 1 win medium\n",
      "Training at episode 50000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7717909306839297  loss: tensor(2.3365)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 60 win random, 0 win basic, 0 win medium\n",
      "Training at episode 52500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7624169900683188  loss: tensor(0.8604)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 57 win random, 0 win basic, 0 win medium\n",
      "Training at episode 55000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7532096256093536  loss: tensor(2.8986)\n",
      "average rewards unscaled in previous 10 games: 60.0\n",
      "300 games greedy evaluation: 62 win random, 1 win basic, 0 win medium\n",
      "Training at episode 57500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7440092852057076  loss: tensor(1.2759)\n",
      "average rewards unscaled in previous 10 games: 40.0\n",
      "300 games greedy evaluation: 64 win random, 0 win basic, 0 win medium\n",
      "Training at episode 60000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.732141231435106  loss: tensor(1.0897)\n",
      "average rewards unscaled in previous 10 games: 10.0\n",
      "300 games greedy evaluation: 61 win random, 5 win basic, 2 win medium\n",
      "Training at episode 62500:--------------------\n",
      "reward unscaled: -100.0 eps:  0.7229162445141987  loss: tensor(1.1355)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 65 win random, 1 win basic, 2 win medium\n",
      "Training at episode 65000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.7167831316069015  loss: tensor(1.6050)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 67 win random, 2 win basic, 1 win medium\n",
      "Training at episode 67500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.707843671055417  loss: tensor(1.2679)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 69 win random, 0 win basic, 0 win medium\n",
      "Training at episode 70000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6962043216339451  loss: tensor(1.8371)\n",
      "average rewards unscaled in previous 10 games: -60.0\n",
      "300 games greedy evaluation: 70 win random, 1 win basic, 3 win medium\n",
      "Training at episode 72500:--------------------\n",
      "reward unscaled: 0.0 eps:  0.6902909256925652  loss: tensor(2.0144)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 61 win random, 0 win basic, 0 win medium\n",
      "Training at episode 75000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6795311408576297  loss: tensor(2.2429)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 72 win random, 2 win basic, 1 win medium\n",
      "Training at episode 77500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6728369400611219  loss: tensor(1.3652)\n",
      "average rewards unscaled in previous 10 games: -30.0\n",
      "300 games greedy evaluation: 66 win random, 1 win basic, 0 win medium\n",
      "Training at episode 80000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6627136101573693  loss: tensor(1.5973)\n",
      "average rewards unscaled in previous 10 games: -20.0\n",
      "300 games greedy evaluation: 67 win random, 2 win basic, 1 win medium\n",
      "Training at episode 82500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6563491499074394  loss: tensor(2.0445)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 58 win random, 1 win basic, 1 win medium\n",
      "Training at episode 85000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6460796602213669  loss: tensor(0.5499)\n",
      "average rewards unscaled in previous 10 games: 60.0\n",
      "300 games greedy evaluation: 68 win random, 4 win basic, 4 win medium\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training at episode 87500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6378625014924376  loss: tensor(1.0090)\n",
      "average rewards unscaled in previous 10 games: 30.0\n",
      "300 games greedy evaluation: 60 win random, 6 win basic, 3 win medium\n",
      "Training at episode 90000:--------------------\n",
      "reward unscaled: -100.0 eps:  0.6327419676001091  loss: tensor(1.8098)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 68 win random, 0 win basic, 0 win medium\n",
      "Training at episode 92500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6219020587445305  loss: tensor(1.2307)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 73 win random, 7 win basic, 1 win medium\n",
      "Training at episode 95000:--------------------\n",
      "reward unscaled: 100.0 eps:  0.6141766289112774  loss: tensor(0.4816)\n",
      "average rewards unscaled in previous 10 games: 20.0\n",
      "300 games greedy evaluation: 69 win random, 1 win basic, 6 win medium\n",
      "Training at episode 97500:--------------------\n",
      "reward unscaled: 100.0 eps:  0.609477795523062  loss: tensor(2.5362)\n",
      "average rewards unscaled in previous 10 games: -10.0\n",
      "300 games greedy evaluation: 67 win random, 0 win basic, 0 win medium\n",
      "Training at episode 100000:--------------------\n",
      "reward unscaled: 0.0 eps:  0.5991023380967283  loss: tensor(2.0680)\n",
      "average rewards unscaled in previous 10 games: 0.0\n",
      "300 games greedy evaluation: 70 win random, 3 win basic, 2 win medium\n"
     ]
    },
    {
     "data": {
      "image/png": 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t5NMlBxwawXA4Io67vtnA87/toHmNCvw1rhflPFxZGFw05SsKC4NDqV6hDIfPnOeL5QeLOzvXtDNxSczcdJzpG48V+Njl+8JxcxF6NqnK89c150xcEt+vPVr4mXSyBbtCWbo3nGcGNaOeT7lL3i/n4cbXozqSlq489lPQFd18RMUn89TMrZyJS7qSLDuk1AWCsh6uNKle/pInjOduC6FJ9fK0qlUx12MHtqxBw6pefLva+dNO7A2N4Y6vN5CUms7Po7vRof6lzVUZ3FxdeLRPIxaN70PXRlX4eMl+Bn60koAPV/D+P3vZfiIqS36TU9P5fNkBrvt0NbtDYnj3ljbMHN2N1rW96de8Got3l4yRHccj49kbGsvoPo24qV1tvlx5iANhscWdrWvKibPxfLv6MHd8tZ4uby3hxTk7efn3XRwr4Gp/K/dF0NnXhwqe7nRs4ENAi+p8vfIQ0fHXTv9WdEIKE+cF07p2RR7s6Zvrfr5Vvfh0ZDuCQ2L49+87L+taERGbxMjJG1iwM5Q9p50/fX6pCwRg9RNkHkIaEpXA5qNnubFt7SxVvexcXISHezVk+8lop7Y5bz8RxZ1fb8DVBWaN6X6hOSs/9XzK8d0DnVn/0gBeG9GaGhU9+XrVYW7831p6vLuMCXN38cfWUwz7bDUfLNrPoFY1WPJsX0Z2qX+hOWxI65pExCax9USU08qXk5jElEIPPot2WzWbIa1r8sqwVniVceOlOTtLRJBzpoPhsUxaeoAbJq2m9/vLefOvPcQkpvDkgKZ8/2BnAP7c7vhkjKejE9gbGku/5tUubHtuSHNiElP5etW107H6/j97OROXxLu3+OPmmvelc0CLGjw9sClztpzipw0Fq0GFRCVwx9frORYZz5QHOl0Yyu5MTl28/mrlV9ubOVtOER6TSPWKnszfEYIqDG+be7NQhls71OWjxfv5ZvURujaqUuh5O3rmPKOmbMS7nDvTH+5G/SqXVj/zU8u7LPf38OX+Hr6cO5/M0r3hLAwOZebmE0xbf4za3p5Mub8TAS1rXHJs/xbVcXcVFgWH0jGHTnNniI5Poed7y2hczYt3bvGnVe3ca2UFsTA4lJa1Kl6owr88tCX/9+sOft58nHu6NiiUc5Qk0fEpvLNgDzPtAREd6lfi30NbMLhVTXyrel3Yr4uvD39sC+GJ/k3yvHHKsGKf1XHav0X1C9ta1qrIiLa1+X7tUR7o6Uv1Cp6FXJrCFXj0LNM3HufhXg1pU9exG7NxA5qy82Q0E+YFc+RMPM8OboZXmbwvuUfPnOeebzcSk5DCjw93oZOvT2FkP1+lskaQ8YvMqBXM3RZC23qVsvyx56ashyv3dmvAkj1hHIoo3CcJ45NTeeynIESEGY9cXhDIrrKXB7d1rMs393Vi66uDmDm6G4ue6ZtjEACo6OlO98ZVWRgcWmTjoTcdPUtcUioHwuMY/vka3l2w94o7diNikwg8do4hrS+W87aOdeneqArvLthLeA6jxkorVWX+jhACPlrJL0EnGd2nEZv+HcCcx3syuk/jS/4vhrerzcHwOPacdqyZbcW+cGp7e9K0evks258Z1IyUtHT+t+zq7rtJTk3npTk7qVOpLM8MaubwcS4uwqS72nN31/p8t/YIgz9edWEIbU72h8Vyx9friU9OZcaj3YosCEApDQStalVEBHadiuFgeCzBITHc6EBtIMN93Rvg4ebClysKr1qrqrz42072hcUy6a72OXZEXalyHm50a1SF8vnclQxuVYOjkfHsDyuaR+Y3HYnEw82F5c/149YOdfhq5SGGfLKKNQfOXHaaS/aEoWo1C2UQEd662Y+k1HRe+3N3YWT9mncqKoGHpwXy5Iyt1PL2ZO4TPfn30JZUr5j7HfoNbWrh5iLM3Z7/g2HJqdaw0X4tql9Se/Ct6sUdnesxY9Nxp4+w+Xjxfv7acfqyjv12zWEOhMfx5k1++d7RZ+dVxo03b2rDr491p5yHKw9O3cyTM7YQEZu1A3jnyWju/Ho9YDUHO1rrKCylMhB4lXGjUVUvdp6KZt62EFwEhmUaD5yfquXL8GBPX34NOsncbYXzlOR3a48yb3sIzw1uTt9mzm8TzMvgVjUQochGD206cpZ29SpRo6In79/WlhmPdsVFhHunbOSZ2ds4ez65wGkuDA6lvk85WtSskGV7o2rl+Vf/Jvy18zRL94QVVhGuOWnpypQ1Rxj00UrWH4rklRta8vvjPS4s4JQXHy8Pejetyp/bQvLtbwk8dpbzyWn0y+VvetyApriI8PGS/ZdVDkeciUti0rIDvP33ngL3D6WmpTN17VH6NquWpWmroDr5+vDXuN48M6gZi4LDCPhwBbM2H0dVCTx6lru/2UA5Dzd+eaw7zWpUyD/BQlYqAwFYHca7TkUzd3sIPRpXzfMOKCfPDW5OF18fXvhtxxX36m84HMnbf+9hSOsaPN6v8RWlVRiqV/Skfb1KFzpbnSkuKZVdITF0bXixGtyjcVUWPNWbfw1owrxtIQR8uKJAATc2MYV1ByPtgHZpG/aYvo1pWr08//ljF+eL6Unx4hQcEs3NX6zljfm76dLQh8XP9OGR3o3y7QDN7MZ2dQiJTiTo+Lk891uxLwJ3V2vYaE5qentyfw9fft96iv15jOiKS0pl1f6Iy2oyXLLbqh2eikpg9cGC1TKX7Q0nPDaJe7tdeZ+Sh5sL4wKasuDp3rSoVZEXftvJrV+uY9SUTVSrUIZfHutOgyr5N087Q6kNBG3qeBMak8ixyHhGFKBZKIO7qwuf39Me77LujPkx6LKHwZ2OTuDJGVtoUKUcH9ze1qHOt6IwpHVNdp2K4eQ551bZtxw7R1q60qVh1vZQT3dXnh3cnL/G9ca3qhdPzdzGdgdHMq3YF0FyWjpD/Grm+L6Hmwvv3tqGkOhEPlzkvDvRq01CchrvLNjDiM/XEhKVwGd3tef7BzpTt3LBmyEHtaqBp7tLvgF6xb5wujT0ybNJZWzfxnh5uPHhon1ZtkfGJTFr83EemrqZDm8s5r7vNvHtZawNsjA4lDqVyuLj5cHMTQV7onnm5hPUqFiG/s0Lr5beuFp5Zj7ajXdvacPB8DgaVCnHrDHdqV2pbKGdo6BKbSDIGJLp4eqS6wUjP9UrePLFPR05HZ3AU7O2FrjamZSaxtiftpCQnMbkUR2p4Ol+WflwhsF22/qiYOc2n2w6chZXF8n1OYnmNSvw48Nd8fHy4INsF4rcLAwOpWp5jzyfvejYwId7utZn6roj7DgZdTlZv6as2h/B4E9W8vXKw9zWoS5LnunL8HyGS+fFq4wbA1vW4K8dp0nJ5Un7U1EJ7A+Lo3/zvJtUKnt58GjvRiwMDmNhcCjfrTnCHV+vp/NbS3jht53sC43l3q4NaF27Ir9vPVWgQQyxiSmsPRjJkNY1ubVDHRbvDrukfT43p6MTWLEvnNs71itQbckRLi7CyC71Wf9SAPOe7EW1CmUKNf0C56dYz16MWtexhij2b1EN77KXfwHu2KAyE4a3ZsW+CD5ZeqBAx06ct5ttJ6L48I62NKle9O2CeWlY1YtmNco7vZ9g05Gz+NXxzvOOsXwZNx7v15jVB86w7lDeVfuk1DRW7ItgUKsauOYwVUhmz1/Xgqrly/D8rztISC6Z009ExiUxftY27vtuE+4uLvz8aDfeu82fSuU8rjjtG9vV4Vx8Sq6d+hmLtfRz4G764d4N8fHyYMyPQbw+fzfR8Sk82b8J8//VizUv9OfV4a24u2t9DkWcZ3cBmmJX7rdrh61rcGfn+qSmK79tOenQsbM3nyRd4c7O9Rw+X0F5lXHDw634L8PFn4NiUtHTnQ9ub8vz17W44rTu6Vqf2zvWZdLSAyzZ7dgd9MxNx/l503HG9mvMdX6Od1QXpSGta7L56FkinfSIe2JKGttORGXpH8jNvd0aUMvbk/f/2ZfnHeG6g5HEJaVeqNHkxbusNYXw3tBYXr7MJ0CvVqrKb0EnGfjRSubvCGHcgCb8/VRvujcuvGdf+jazbqJyax5asS+CupXL0rha+Rzfz6x8GTc+v6s9r9zQkhXP9WPh+D48M7g5fnW8L9RahvpZo5XmbXP8YbaFwWFU8fKgk68PTaqXp4uvD7M2n8j3d52WrswOPEHvplWdMoLvalNqAwFY48od+SPNj4jwxk1+tKnjzfhZ2zhyJvfH72MTU/gt6CSvzg2md9OqPDe4+RWf31mGtK5JusLSPMY+X4ltJ6JITkuniwPjpT3dXXkqoCnbTkSxZE/u+VkYHEr5Mm70cPCC179FdesJ0K2n+LGAT4Berc7EJXHvlI08+8t2GlUrb41WGdwcT3fXQj2Ph5sLQ9vUZNHusEtqVEmpaaw9eIZ+zas53PzUo0lVHundKNfneSp7edCnWTXmbc9/tFJGHpbvDWdgy4u1w5Fd6nHkzHk2HM57ZoDVByI4FZXAyM71Hcr7ta5UB4LC5Onuypf3dsDNVRjzY2CW0SgZk3U9+P0mOr6xhGd/2U5dn7JMGtk+3+aL4tS6dkXqVCrLIic1D206chYR6OzggzO3daxLw6pefLBwH2k5XAjS0pXFu8Po17waZdwcv+iNG9CUgBbVef3P3QQevbanq05JS2fsT0EEHTvHGzf58csY5w5HHNG2DvHJaSzJNhQ38Og54pPT8u0fKKgb29XmdHQigcfyHq0EsO6QVTsc4nfxocKhbWpR0dMt32mwZ246QRUvDwa1yvnBy5LGBIJCVLdyOT67qwMHw+N4dvZ2ptidXhmTdR0Ij2NU9wbMHtOdxeP7UtnryttpnUlEGNy6BqsOnHHKMMtNR87SomZFvMs51kfj5urCM4OasS8slnk5PMy05fg5Is8nZ3mIzBEuLsJHd7ajTuWyjJ2+5ap46jg9XS9rqORbf+1h89FzvHerP6O6NchxSvXC1KWhDzUrejI3W3PN8r3heLi6FGpTFFgTP5Z1d3VoOPGi4DC8PFyzzNrr6e7Kze3rsGBXKFHxOT+fEhGbxJI9Ydzase5V0X5fFEpHKYtQr6ZVef66FvwTHMobmTq9/hrXi9XP9+c/w1rRpaHPVV0TyGxwq5okp6azspAX2khJSyfo2DmH+gcyu6FNLVrVqsjHiw9csoDOwl2heLi6ONQ5mZ13WXe+HtWRuMRUHp++pVgX59l+IoobPltD93eWFmhE0+9bTzJ13VEe7tXwklX2nMXVRRjmX4uV+8OzXFhX7I+gayMfynkU7nRmXmXcGNiqBn/tPJ3n7+hi7bD6JU1iI7vUJzk1nTlbcg4mvwadJDVdndpJfLUxgcAJxvRpxLf3dcrS6dW6tvdV84xAQXT2rUzlcu6FPnpo16loElLSLnl+ID8uLsL/Xdec42fjmRV4cbU4VWXh7lB6NKly2cNwW9SsyHu3+RN47Bxv/VX0U1CcT0rl9T93c/MXa4mMS6Kchxt3f7PRoZlug0OieWnOTro29OHF6698AERB3NiuDilpemGdjhNn4zkYHke/Qm4WunC+trWJik9hzcHcb062Hj/HmbgkBre+tGmnZa2KtK1XiZn2k72ZqSqzNh+nS0OfQuk/vFaYQOAEIsLAVjUcmsTuaufm6sLAljVYtje8UO+SMy5ujvYPZNavWTU6+1bms6UHLnRS7jkdy4mzCQVuFspuRNvaPNKrIdPWH+O3IMeGGRaGZXvDGPzxKr5be4S7u9ZnybN9+XVsd6pXLMN9323Mc+nDqPhkHvspiEplPfj87g64F/KY9/z41alIo6peF0bzrLDzWpgPYWXWxx6tlNfooYXBobi7Sq7TQtzVuR77w+LYku3J6PWHIzkaGc9dXUpPbQBMIDAcMKR1TWITU9lQiEtYbjpylkbVvC7rQRoR4fnrWhAem8S09UcB6x9fxGpDvlIvXt+Cbo18+PfvOy9ZwKiwhccm8sSMLTw0NZByHq78+lh33rypDRU93anlXZbZY7rTsGp5HpkWmGOtLC1dGTdzG2HRSXx5b4dieTBJRBjRrjYbjkQSGp3Iyn3h1PcpR0Mn3QhZo5VqsWh3WI7LXaoqC4PD6NG4KhVzqR0Ob1sbLw9Xft6UdQ3ymZtOUNHTjeuv0iHdzmICgZGvXk2rFuoSlmnpyqajZwvcP5BZZ18f+jevxpcrDhGdkMLC4FA6NahcKBdCN1cXPr+7A5XLefDYT0GXNeldfjKaIAZ+uJLFwWE8O6gZf43rfcnUw1XLl2Hmo91oVbsij0/fckkn6SdL9rNqfwQTR7SmfR5PUjvbiLa1UYXftpxk7cFI+hdg2OjluLFdbXu00qVDifeFxXL8bHyOzUIZvMq4MaJdbebvCCEm0Zoe5tz5ZP7ZFcotHeoW+lDbq50JBEa+PN1dC3UJy72hMcQmptK14ZWNKHluSHOiE1J4de4u9obGXnGzUGZVy5fhq1EdCY9J4rpPVvHPrsubwjg387aH8MJvO2lRqyILnu7NvwKa5jpCxbucOz890pXOvpV5etY2frbny1kUHMpnyw5yZ6d6xd6U0ahaedrU8eZ/yw+SkJLmtP6BDF18rdFK83IYPbRwVxgi5Dv0c2Tn+iSmpF8Y8TRn6ymS09IZWcqahcAEAsNBg1vVJLyQlrDM6B8oaEdxdq1rezPMv9aFf+TBrQovEAC0q1eJX8d2p0r5Mjz20xZG/xBIaPSVDy2Nik/m9T9307auNz8/2s3hJ2+nPtiFvs2q8dKcnbyzYA/PzN6Of11vXrux9VUxECHjLr2MmwvdnLB6X2YuLsLwtrVYuT/ikmGgC4ND6VC/cr6rnvnX9aZlrYrM3GR1Gs/cdJx29SrRombhrJB3LXFqIBCRSiLyq4jsFZE9ItJdRHxEZLGIHLC/F1991nBY/xbVcXMRJszbxU8bjl3RWPtNR85St3LZQplt8dnBzXF1EVrUrFAoK7pl51+3EvOe7MlL17dg1YEIBn60kh/XH72imtHbf+8hKiGFd27xL9AwYk93VyaP6sTQNjX5euVha3GkezteNc0Yw/xrIwLdGlWhrIfz85R9tBJYI5Z2n47JsjJdbkSEu7rUIzgkhh/WH+NAeFyx16yKi7NrBJ8C/6hqC6AtsAd4EViqqk2BpfZr4yrnXdad12/0Iy4xlVf+2EXXd5Zyyxdr+XrlIY7mMaVGdqrKpiNnr7g2kKFhVS/+e5s/rw5rVSjp5cTd1YUxfRuz8Ok+tK9fif/MDea2r9axL9SxpRozW38oktmBJ3mkd8PLWpvZw82FSSPb8+L1Lfj+gc7UKcapi7Or6e3JWze1YXwBlnO8Eq1rV6RRNa8s/SaL7Lm+HK0d3tiuDp7uLrwxfzdeHq4M8y/4lPQlgThroi0RqQhsBxppppOIyD6gn6qeFpFawApVzXPCnU6dOmlgYKBT8mkUjKqyPyyOhcGhLAwOJTjEmgmyRc0KDG1Ti9F9GuV5h3owPI6BH63kvVvbcOc1OI+LqvLHtlO8MX8PMQkpjO3XmKcHNnPozj4xJY2hn64mJT2dRU/3LZK75pLukyX7+XTpAda9OIBa3mW54+v1RMensHB8H4fTeGb2NuZsOcVdXerzzi1tnJjboiciQaraKb/9nFkjaAREAN+LyFYR+VZEvIAaqnoawP6eY6+SiIwWkUARCYyIKNynWo3LJyI0r1mBcQFN+Wtc7wtPS1cs685Hi/czcV5wnsdf7B9wbhuys4gIN7e35vMf0a42ny07yFMzt+Y6J39mXyw/yOEz53nrpjYmCBSSjNFK87efJjIuicCjZx1qFsrsgR6+VCrnzn3dr3wVsmuVMwOBG9AB+FJV2wPnKUAzkKpOVtVOqtqpWrXiXcPXyF09n3I83Kshs8d058n+TZi5+cSFUS052XQkkmoVyuDrhPb8ouTj5cFHd7TjpetbMH/Hacb+FJTn3EAHwmL5cuUhbmpXmz7FvCZ1SdKoWnn863ozb3sIS/aEka44NAV5Zv51K7Ht1cG0rFX6OokzODMQnAROqupG+/WvWIEhzG4Swv7unDmOjSI3flAz+jSrxoS5wWzNYS1bVWWj3T9wNYxyKQxj+jbmjZv8WLInnIenbc5xcr70dOWlOTvxKuPGK07syyitRrStzc5T0UxZc4Q6lcrS+jL6Xko7pwUCVQ0FTohIRvt/ALAbmAfcb2+7H5jrrDwYRcvVRZg0sh01vMsw9qctlywJePJcAqejE+lWSB3FV4tR3Rrw4e1tWX8okvu+20R0Qtb1q2duPkHgsXO8PLQlVcsX75KEJZG15CbsD4tjSOuaJeYmoyg5e9TQv4DpIrIDaAe8DbwLDBKRA8Ag+7VRQlQq58FX93bkXHwyT87YkqXtfOM13j+Ql1s71uV/d3dgx8ko7v5mw4VV3cJjEnlnwR66N6rCbR3rFnMuS6YaFT3pZv9N5fU0sZE7pwYCVd1mt/P7q+pNqnpOVSNVNUBVm9rfr+2VQIxLtK7tzbu3tmHjkbO8u2Dvhe2bjkRSqZw7TauXzFkdr29Ti8n3deJgeBx3Tt5AWEwir/25m6TUdN662c/cqTrRmL6NGNyqBp0amMeSLkfhThZuGLab29dl+wmr3da/rjc3tqvDpiNn6ezr4/TFUopT/+bVmfpgFx6ZtpkbJq3hTFwSzw5qRqNSNKVxcejXvLrTp7UoycwUE4bT/HtoSzr7VuaF33awcn8ERyPjr2iiuWtF98ZV+OmRriSnptGsRnnG9G1c3FkyjDw57YGywmQeKLt2hcckMuyzNUTFp5Ccls68J3viX7dScWerSJyJS8LdxcXhpTgNo7AV2gNlYrlXRF61X9cXkS6FkUmj5Kte0ZMv7+1AuipeHq60KkVjtauWL2OCgHFNcKSP4AsgHRgAvA7EAr8BnZ2YL6ME6djAh0l3tefs+WTcinj1LMMw8udIIOiqqh1EZCuAqp4TEQ8n58soYYa2KV0rPhnGtcSR27MUEXEFFEBEqmHVEAzDMIwSwJFAMAn4HaguIm8Ba7AeDDMMwzBKgHybhlR1uogEYU0RIcBNqrrH6TkzDMMwikS+gUBEfLAmhvs50zZ3VU3J/SjDMAzjWuFI09AWrHUF9gMH7J+PiMgWEenozMwZhmEYzudIIPgHGKqqVVW1CnA9MBt4HGtoqWEYhnENcyQQdFLVhRkvVHUR0EdVNwBmTl3DMIxrnCPPEZwVkReAmfbrO4Fz9pBSM4zUMAzjGudIjeBuoC7wB9YiMvXtba7AHU7LmWEYhlEkHBk+egZrgZmcHCzc7BiGYRhFzZHho9WA54HWgGfGdlUd4MR8GYZRiqWkpHDy5EkSExOLOyvXBE9PT+rWrYu7++VNcuhIH8F0YBYwDHgMa53hiMs6m2EYhgNOnjxJhQoV8PX1NSu75UNViYyM5OTJkzRs2PCy0nCkj6CKqk4BUlR1pao+BHS7rLMZhmE4IDExkSpVqpgg4AARoUqVKldUe3KkRpDxBPFpEbkBCMHqPDYMw3AaEwQcd6WflSOB4E0R8QaeBT4DKgJPX9FZDcMwjKuGI01D51Q1WlV3qWp/Ve0InHV2xgzDMEoSX19fzpw5U9zZyJEjgeAzB7cZhmGUSKpKenrJfX4210AgIt1F5Fmgmog8k+lrItbDZIZhGCXW0aNHadmyJY8//jgdOnTg4YcfplOnTrRu3ZoJEyZc2M/X15cJEybQoUMH2rRpw969ewGIjIxk8ODBtG/fnjFjxqCqF4756KOP8PPzw8/Pj08++eTC+Vq0aMEjjzyCn58f99xzD0uWLKFnz540bdqUTZs2Oa2sefUReADl7X0qZNoeA9zmtBwZhmFk8tqfwewOiSnUNFvVrsiE4a3z3W/fvn18//33fPHFF5w9exYfHx/S0tIICAhgx44d+Pv7A1C1alW2bNnCF198wQcffMC3337La6+9Rq9evXj11Vf566+/mDx5MgBBQUF8//33bNy4EVWla9eu9O3bl8qVK3Pw4EF++eUXJk+eTOfOnZkxYwZr1qxh3rx5vP322/zxxx+F+jlkyDUQqOpKYKWITFXVY045u2EYxlWsQYMGdOtmjZafPXs2kydPJjU1ldOnT7N79+4LgeCWW24BoGPHjsyZMweAVatWXfj5hhtuoHLlygCsWbOGm2++GS8vrwvHrl69mhEjRtCwYUPatGkDQOvWrQkICEBEaNOmDUePHnVaOR0ZNVRGRCYDvpn3N08WG4ZRFBy5c3eWjIv1kSNH+OCDD9i8eTOVK1fmgQceyDJuv0wZayJmV1dXUlNTL2zPaVhn5iai7DLSAXBxcbnw2sXFJUu6hc2RzuJfgK3AK8D/ZfoyDMMoFWJiYvDy8sLb25uwsDAWLFiQ7zF9+vRh+vTpACxYsIBz585d2P7HH38QHx/P+fPn+f333+ndu7dT858fR2oEqar6pdNzYhiGcZVq27Yt7du3p3Xr1jRq1IiePXvme8yECRO466676NChA3379qV+/foAdOjQgQceeIAuXboA8Mgjj9C+fXunNv3kR/KqpgDYo4TCgd+BpIztqlpkzxJ06tRJAwMDi+p0hmEUsz179tCyZcvizsY1JafPTESCVLVTfsc6UiO43/6euTlIgUYO59AwDMO4ajmyHsHlTWdnGIZhXBPy7SwWkXIi8oo9cggRaSoiw5yfNcMwDKMoODJq6HsgGehhvz4JvOm0HBmGYRhFypFA0FhV38eejlpVEwAzP6xhGEYJ4UggSBaRslgdxIhIYzKNHjIMwzCubY4EggnAP0A9EZkOLMVaw9gwDKPEOnr0KH5+fleUxrx583j33XcLKUfO48ioocUisgVreUoBnlLVq3NSbcMwjKvIiBEjGDFiRHFnI1+OjBq6Gevp4r9UdT6QKiI3OT1nhmEYxSw1NZX7778ff39/brvtNuLj43n99dfp3Lkzfn5+jB49+sLcQZMmTaJVq1b4+/szcuRIAKZOncqTTz4JQFhYGDfffDNt27albdu2rFu3rtjKlZ0jD5RNUNXfM16oapSITAD+cOQEIuIKBAKnVHWYiPgAs7AmsTsK3KGq5wqYb8MwSosFL0LozsJNs2YbuD7/Jpt9+/YxZcoUevbsyUMPPcQXX3zBk08+yauvvgrAqFGjmD9/PsOHD+fdd9/lyJEjlClThqioqEvSGjduHH379uX3338nLS2NuLi4wi3TFXCkjyCnfRwJIBmeAvZkev0isFRVm2L1N7xYgLQMwzCKTL169S7MK3TvvfeyZs0ali9fTteuXWnTpg3Lli0jODgYAH9/f+655x5++ukn3NwuvUQuW7aMsWPHAtYspd7e3kVXkHw4ckEPFJGPgP9hjRz6FxDkSOIiUhe4AXgLeMbefCPQz/55GrACeMHhHBuGUbo4cOfuLNmnkRYRHn/8cQIDA6lXrx4TJ068MB31X3/9xapVq5g3bx5vvPHGhQBxLXCkRvAvrAfKZgGzgQTgCQfT/wRrhFHmxT5rqOppAPt79ZwOFJHRIhIoIoEREREOns4wDKPwHD9+nPXr1wPw888/06tXL8BakSwuLo5ff/0VgPT0dE6cOEH//v15//33iYqKuqTpJyAggC+/tCZyTktLIyamcFdduxJ51gjs9v25qjqwoAnb01CEq2qQiPQr6PGqOhmYDNbsowU93jAM40q1bNmSadOmMWbMGJo2bcrYsWM5d+4cbdq0wdfXl86dOwPWhf3ee+8lOjoaVWX8+PFUqlQpS1qffvopo0ePZsqUKbi6uvLll1/SvXv3YijVpRyZhnoeMEpVowuUsMg7wCggFfAEKgJzgM5AP1U9LSK1gBWq2jyvtMw01IZRuphpqAvuSqahdqRpKBHYKSJTRGRSxld+B6nqS6paV1V9gZHAMlW9F5jHxamt7wfmOpAHwzAMw0kc6Sz+y/4qLO8Cs0XkYeA4cHshpm0YhmEUkCNPFk+z5xqqr6r7LuckqroCa3QQqhoJBFxOOoZhGEbhc+TJ4uHANqz5hhCRdna/gWEYhlECONJHMBHoAkQBqOo2wKxaZhiGUUI4EghScxgxZIZzGoZhlBCOBIJdInI34GovU/kZcPXMlmQYhnGV69evHxlD4IcOHZrjXETFydEni1tjLUYzA4gGnnZingzDMEqsv//++5KHzYpbroFARDxF5Gngfaxhnt1VtbOqvqKqiUWVQcMwjOJw9OhRWrRowSOPPIKfnx/33HMPS5YsoWfPnjRt2pRNmzZx/vx5HnroITp37kz79u2ZO9d6LCohIYGRI0fi7+/PnXfeSUJCwoV0fX19OXPmzCUL33zwwQdMnDgRsGoQ48ePp0+fPrRs2ZLNmzdzyy230LRpU1555ZVCL2tew0enYa1TvBq4HmiJqQkYhlHE3tv0HnvP7i3UNFv4tOCFLvnPdXnw4EF++eUXJk+eTOfOnZkxYwZr1qxh3rx5vP3227Rq1YoBAwbw3XffERUVRZcuXRg4cCBff/015cqVY8eOHezYsYMOHToUOI8eHh6sWrWKTz/9lBtvvJGgoCB8fHxo3Lgx48ePp0qVKpdT9BzlFQhaqWobABGZAmwqtLMahmFcAxo2bEibNm0AaN26NQEBAYgIbdq04ejRo5w8eZJ58+bxwQcfAJCYmMjx48dZtWoV48aNA6zpqf39/Qt87oyVzdq0aUPr1q2pVasWAI0aNeLEiRNFFghSMn5Q1dTs07EahmEUBUfu3J2lTJkyF352cXG58NrFxYXU1FRcXV357bffaN780unS8rtmurm5kZ5+cWLmjOmss58783kzn7sw5dVZ3FZEYuyvWMA/42cRuXrmTzUMwygmQ4YM4bPPPruwXOXWrVsB6NOnD9OnTwdg165d7Nix45Jja9SoQXh4OJGRkSQlJTF//vyiy3g2udYIVNW1KDNiGIZxrfnPf/7D008/jb+/P6qKr68v8+fPZ+zYsTz44IP4+/vTrl07unTpcsmx7u7uvPrqq3Tt2pWGDRvSokWLYiiBJd9pqK8GZhpqwyhdzDTUBefsaagNwzCMEswEAsMwjFLOBALDMK5K10Kz9dXiSj8rR6ahvkVEDohItBk1ZBhGUfD09CQyMtIEAweoKpGRkXh6el52Go6sUPY+MFxV91z2WQzDMAqgbt26nDx5koiIiOLOyjXB09OTunXrXvbxjgSCMBMEDMMoSu7u7jRsaJY9KSqOBIJAEZkF/IE1AykAqjrHWZkyDMMwio4jgaAiEA8MzrRNARMIDMMwSgBHFq9/sCgyYhiGYRSPXAOBiDyvqu/bK5Jd0nWvquOcmjPDMAyjSORVI8joIDZzOxiGYZRgeQWCxiLSGZiuqoU756lhGIZx1cgrENQFPgVaiMgOrAXr1wLrVfVsUWTOMAzDcL68pqF+DkBEPIBOQA/gIeAbEYlS1VZFk0XDMAzDmRwZPloWawipt/0VAux0ZqYMwzCMopPXqKHJQGsgFtiI1TT0kaqeK6K8GYZhGEUgr0nn6gNlgFDgFHASiCqCPBmGYRhFKK8+guvEWn25NVb/wLOAn4icxeownlBEeTQMwzCcKM8+ArXmgN0lIlFAtP01DOgCmEBgGIZRAuTVRzAOqybQE0jBHjoKfIfpLDYMwygx8qoR+AK/AuNV9XTRZMcwDMMoann1ETxTlBkxDMMwiodZs9gwDKOUM4HAMAyjlDOBwDAMo5QzgcAwDKOUM4HAMAyjlDOBwDAMo5RzWiAQkXoislxE9ohIsIg8ZW/3EZHFInLA/l7ZWXkwDMMw8ufMGkEq8KyqtgS6AU+ISCvgRWCpqjYFltqvDcMwjGLitECgqqdVdYv9cyzWGsh1gBuBafZu04CbnJUHwzAMI39F0kcgIr5Ae6x1DWpkTFlhf6+eyzGjRSRQRAIjIiKKIpuGYRilktMDgYiUB34DnlbVGEePU9XJqtpJVTtVq1bNeRk0DMMo5ZwaCETEHSsITFfVOfbmMBGpZb9fCwh3Zh4MwzCMvDlz1JAAU4A9qvpRprfmAffbP98PzHVWHgzDMIz8ObJ4/eXqCYwCdorINnvbv4F3gdki8jBwHLjdiXkwDMMw8uG0QKCqawDJ5e0AZ53XMAzDKBjzZLFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRilnAkFJk5YCaanFnQvDMK4hJhCUNDPvgc87wbmjxZ0TwzCuESYQlCSHlsOBhRB1DL67HiL2F3eODMO4BphAUFKowtLXwbsePLIE0lPg++vh9I7izplhlFzpafD38xCytbhzckVMICgp9s6HkC3Q70Wo0xEe/AfcysC0YXBic3HnzjBKpj1/wqavYf4z1s3YNcoEgtykpUDoLueknRQLZw4UXnrpabDsTajaDPxHWtuqNoEHF0BZH/jhRjiyqvDOV5IkxUH0yeLOhVGc0lIhZFvBL+SqsO4zcPWwbsL2zr9kl9T0VI7FHCucfDqRCQQ5SY6HGXfCVz1hy4+Fm3bMafh2IHzRDY5vLJw0d8yCiL0w4BVwdbu4vXIDeOgfqFQPpt8O+xcVzvlKClWYeRf8rxvEhBR3boziELINvh0Ak/vCzl8KduyJjXAqEAa9bt2ELXvTuinL5M0NbzL89+HsidxTeHl2AhMIskuMgem3waFlULU5/PUsnNpSOGlHHbfa7aNPQoXaMPs+iA29sjRTk2D5O1CrHbQccen7FWrCA39DteYw824I/uPKzleSHF5u1ZSSY+Hv/yvu3BhFKfk8LHoFvhlg/Q9WbgjL34LUZMfTWPcZeFaCDvdZN2ERe62bMtvm0M38duA3FGXS1kmFX4ZCZAJBZvFnrWaUExvhtilW00r56jBrFJw/c2VpnzkI310HCWfhvrlw90xIioHZ9xfsjy+7oGkQfRwCXgWRnPfxqgL3/2n1Hfz6IOxfePnnKykudK7Xh/4vW9X6PZdW7a9KKYlWjTXwu+LOybXp4BKrRr7uM+si/sQmuP59a8j11h8cSyPyEOz9Czo/Ah5e1k1YrXbWTVlqEslpyby+/nXqlK/D2LZjWXNqDUFhQc4s1RUxgSBDXDhMHQZhwXDnT+B3q3UBvfNHOB8Bvz50+Q9qhe6yagKpSfDAX1C3E9RoDSM+gxMbrDuTy5F8Hlb9F3x7Q+MBee/r6Q2j5kC1lvDn01bNpzTb86c10qPfi9BrPNTws2oF18LnsvpD2P+PVVs9vLK4c3PtiIuA3x6Bn24FN0/rRm/4J1C2EjQdBPW7w8r/Wk3D+dnwBbi6Q5fR1msR62Ys+jgETePbnd9yNOYo/+n2Hx70e5BqZasxacsk9CrtUDaBACDqhHW3fu4I3DObtKaD2RK2hSk7p/DNmUC+6XI735zdyjdz7+GbHd9c+FoXsi7/tE8GwdQbwMXNaq+v2ebie21ug+5PWqMOts8seL43fgXnw7PUBoIjg9kcmssoIQ8vGDEJYk9b7ZmFKD4lnnmH5pGQmlDwg1Wt8h9eYXXSZxN2Pox/jvxTeP9EFzrXm0PbkdY/9PCMz+WNwjmHs4TvhTUfW3egVZtZNbyoE8Wdq6vehrXvsfSbbsTvmQf9XoLH1kCDHhd3yLiQx4XCpsl5JxZ/FrZOB/87oEKNi9sbDwDf3hxe+wHf7PyGoQ2H0rNOT8q6leWxto+xJXwLq0+tdk4Br5Bb/ruUcJGH4IcbSU6MYePQN1gauoLlmydwNvFs1v18KkHsbti6O8vmwQ0G81LXl6hatuqlaR9dCzPuAK+qVnNQZd9L9xn4GpzeDn8+BdVbQq22juU74Rys/RSaXQ/1ugBwJPoIDy98mITUBCZ2n8jNTW++9Li6naDLo9Yfu/8d1usrpKq8uu5VFh5dyJwDc/h8wOeU9yjveAIbvoCF/7Z+9qwEza+HFsOg8QCOJUbwyKJHCD0fSkRCBKNajbri/LJ9JpzZB3f8AC6u1ra6Ha27u02Twf/OQvlcCl16uvV3UqY83PARJEZZbdyzR1nDhd09izuHV6Xv5z/MR5GbwKccnlUr0T09hIHHFtK3bl+8y3hf3LFBD2gyyAq0HR+wago52TwFUhOsm7jMREgf8AqvLRhFOVx4vvPzF966ucnNfL/reyZtmUSvOr1wkavrHvzqyk0RO38qiH9m3MDz5dLoU68Gj+/4lAVHFtClZhf+2+e/rBm5hi33brG+Rm5gS0p1tpyMZMuQGWy8eyP/av8vVpxYwYg/RvDr/l9J1/SLiR9cYlVBK9ax/klzCgJgjfK57XtrmOese627DUes/dRqxhhgNSudTznP08ufxsPFg841OvPquleZvmd6zscO+A9UqGVdVLLdgaemF7z564fdP7Dw6EIGNRjE9vDtPLroUaKToh07+MhqWPQf68J/53Rodh3s+xtm3cP+j5tx/+83kZwUQ9fqHfgw8MPcazuOSk2CFe9C7faXdq4PeMX6XOaNy7FmUuy2TLOaEge/CeWrQdWmcPNXVhPX388W2Tj2tGwjY65Wmp7O57+P5KPITVznWplvAr7k5ma3sjtyNy+veZm+s/ryyKJH+Hnvz4SdD7MOCviPFWDXfZZzoimJVg2+ySDrxi2bOYkn2eLpyXNno6iS6fLq7urOE+2fYN+5fSw6WoDRe5GHClDiK6CqV/1Xx44dtTClpafp7I0favfvWqvfVD/tM6OHvrr2VV15YqUmpibmfmD0KdX3m6hO6qCaEKWqqkeijuiD/zyoflP99P4/R+qhFW+qfne96sRKql/2Uo2LcCxTJzarvl5V9YebVNNS89435rTqGzVUf31YVVXT09N1/PLx6j/NXzeGbNSk1CR9atlT6jfVTydvn5xzGrv/VJ1QUXX1R6qqejzmuI5ZNEZ7/txTt4ZtdSzPqrohZIP6T/PX8cvHa3p6ui47tkzb/9Beb557s0bE51P2qJOq7zVSndRRNSH64vbUZN259XvtOa29DpjSWg+96aOxb9fRYbMCtM/MPno67rTD+bs0w19Z5T64NOf398y33l/14eWfwxliTqu+XU/1+xtU09Ozvrf0DSvPm79zahaS05L1+ZXPa9+ZfXXXmV1OPdeVSk9L0/dmj1C/qX76nxkBmpqSdPG99HTdGbFTPwn6RIfNGaZ+U/207bS2+su+X6wdZj+g+mYt1diwSxMOmmZ91oeWX/JWRHyEdp/eXR+ce4emT/BWXTwhy/tp6Wl689yb9YY5N2hyWnL+hdjyo+rEytb/6mUCAtWBa2yxX+Qd+SrMQHDo3CG97/eb1G+qnz74jZ9u2jdXU/O78GZ2dK3qaz6qM+5STUtTDQ3W9OXv6pzJnbXHlJba/vvW+sXkDpq09A3V+HMFy1zg99Yf2ZLX8t5v/rNWHiIPqarqlJ1T1G+qn07dNfXCLilpKfrCqhfUb6qffhL0iaZnv3ioqv58t6a8UUO/2/hf7fRjJ+3yUxcd9Msg7fxTZ90QsiHf7J6OO629f+6tI34foXHJcRe2rzu1Tjv/1FlvmHND7hftlETVyf1V36qtGr43y1ubT2/WrtO76pBfh+jxqGOqxzepft5FD71TS7v80FHvnn+3JqUm5ZxuXhJjVd9vnPPFNLOf71Z9o/qFz/eqMOs+1derqUYcuPS9tFTVH29Rfa2K9Vk5QWJqoj655En1m+qnvX7upd2md9MtYVuccq4rlZqSpBNmDFK/qX76zqxhmpaakuf+h84d0jGLx6jfVD/9IfgH6zOeWFn17+ez7piWpvpZZ9Uve+b49/Pciue0/Q/t9XDUYesm7Y0aqjGhWfZZdmyZ+k31uxh0cpNxw/LDTapJcXnvmwdHA4HoVdqLnVmnTp00MDDwitJITku2On93fE3ZtBSeS/LgppF/IpXqFjyxDV/BPy9A+RoQFwYI1OvCmSYDeD/5OAtCVtPIuxF3t7ib/vX7U71cdcfTnjfOagJoMcx6YvESag1z7DAKhn3MupB1jF0ylkENBvHfPv9FMg0hTdd03tjwBr/u/5W7W9zNC11eyNI2uevYcl5b9Dh7PdzoV7cfL3d7GVdxZfTi0RyPOc7H/T+mT90+OWYzKS2JBxY8wJGYI/x8w880jI+BLT9Ap4ehRiu2hm/l8SWPU9GjIt8M/ob6FetnTeDPpyBoKtzxI7S62ESz9tRanl7+NLXK1+KbQd9Qw8vujDsfCT/dzJKYw4yvXpnbm93Oq91fdfxzBVj1gdUZ/PASqNc59/1iQuDzLla/wag/ch+WW1T2/QM/3wn9X4G+/0d0UjTf7vyW0POZnkFJS7aefdF0aDwAT89K9Kjdg951el/sr0lPh1NBVtObV1Xo/Ci45fQ3llV8Sjzjlo9j0+lNvNLtFfrU7cOjix4lLD6MT/t/Svfa3R0rx8kg2P4z1O0MzYbk3gafXVKc1dR6ZKXVwd/iBushyRykpMTz8uyhLEiN5NEKLfnXTTMRl/xbwJPTknlx9YssPraYJ9o9wZiju5Ads+BfQVDJ/tvdvwhm3A43T4a2d2Y5ftXJVTyx9AmeaPcEj7V9zGrS+V8X6Pgg3PDBhf1UlVELRnH6/Gn+vuVvyriWuTQzqz+Epa9zsNlAPqhaldd6vn7x/6CARCRIVfPt8CoVgSAoLIjX1r/GkegjXH8+gRekGlVGzbPaWS+HqtW5eWa/9UfZ/IYsowdWnVzFh4Efcjj6MAD+1fwJqB9AQP0AGlRskHfaqUnw+5i8p7coUwHu+plTksbI+SOpWrYq04dOp5x7uRyyqnwQ+AE/7P6BGxvfyGs9XiMpLYnPtn7GjL0zqOJaln+fOkrAkE8R+487KjGKMUvGsP/sft7p8w7X+V53SboT103ktwO/8Unv9wg4uA7WfwGaBi7u0Otp6P0cu2MOM2bxGNxc3Phm0Dc0qdzEOjhoGvw5zhq2OXDihTSXHlvKc6ueo0mlJnw18CuqlK2S9aSJ0TD9Dj6J28uUShV5rcdr3NL0lrw/zwzxZ+HTduDbE+76Of/9N30Dfz+X4z99kUqKs8a8e3iho1ex4MRS3tv8HtFJ0dSrkO1imJZkjSBy8yTKszxRSVG4u7jT1bsJAcnQ/9h2qsSGgrhav6vqrWD4pxcGG+QkJjmGx5c8zs4zO3mz55sMbzwcgDMJZxizeAxHoo/wYd8P6V+/fx5liLVGaW38GsTF/jtxs4Y9txxm3fRUqJn1mPORsH+BddNzaJlVNvdykGIP7azVzjq25QjrYUkgKSmG/5t1Pcs1hqd8OvLI8KkF+qhT01OZsG4C8w7N48EmtzF+2f+QNrfDTf+zdpg23Hoe6Okd1kgzW3xKPDfPvZmybmX5ZfgvuGe89+fTsPUn+Fdglj7CzaGbeWjhQzzX6Tnub33/xQzYz7Ykrf2IyU27811aGOXdy/Nh3w/pUiv331FeTCDA+iP+OOhjft3/K7U9vHnlxCF6V24F9/wC5XyckNOLVJXD0YdZenwpS48vZXekNdqoSaUmDKg/gE41OuHmkvugrcaVGuPjmXseE1MTuW/BfZyIPcHMYTPzDDCqypfbv+TL7V/Ss05PDkcd5vT509zZ/E6eavckFX66Dc4ehicDL3wuscmxPLn0SbZFbLtkBNKv+3/ltfWv8WjdQYzbudh6YrrD/dDzKVj5PuyYCT6NYfinHKxUi9GLR5OSnsKE7hPwjg2zxuvX8IPBb1gXBuDAuQO8v/l9WldtzRcBX2QdzZFZ8nnSfr6LxxJ2s6VceaYN/Qm/qn65lv2CxROsDvaxa61nOGxnEs5wJPpITh8a/P0cEhNC0/sW4F2laf7nKIi0FAjfY3X4upfNfb9//g0b/sepu2bw5on5rDm1Br8qfkzsMZHmPs0v3X/HbJjzKGmtb2W7xrM0YgtLy7hyyt0NAdp71SOgyY0ESHnqLH0bYk5ZD0UFvAqeFbMkdTbxLI8tfowDUQf4b5//MrDBQCugxoVDteZEJ8cwdslYdkfu5u1ebzO00dBL87NvgfW8Q0wIiZ0f4miHu2mckor7vr+ti/xZuzO0bmcrILh5Wg/3HVtr1W4q1oWWw0hrPpS95SsTf+4wHF8Hx9ZbI7/A2qd+N76JDGQ9CbxUow93X/e/y/q1pGs6b298m1n7ZnFnWV/+vWcNLo9vgNRE+LqPNZ1Ez6ey7P/+5veZvmc6066bRocaHS4mFhMCk9pDy+HWDUWmmsnoRaPZc3YPC25ZYNXY0tPhnxfZvGMqr9dpyNH0eIY3Gs5znZ/L8zqQn6s6EIjIdcCngCvwraq+m9f+lxsIXlj1Av8c/YdRPh14POgPyjWw7wbLVLi8jF+B03GnWXZiGUuPLyUoLCjrCKMcuIgL7au3v1CTqF2+9oX3VJVX1r7CvEPz+GzAZ/Sr18+hPEwLnsYHgR/Q2LsxE3pMoH319tYbYcHWH7n/yIt3P1h3Ok8vf5r1p9fzUpeXuLvl3eyI2MED/zxAFynH/w7uxLVKU+uu0rfnxRMdWgbzx1tParYfxfHuo3l01f8Rcj7v+Xy61OzCpAGT8HL3yrsgKYmc+2UUdybsQj29mXXr33n/s8SGWrWBlsPh1m84EXPiQoDeHrEdJe//ATdVOks5Amp2Y0D7MVSr3jrP/XOVHG99Nnv+tB4IS4yy7nKbDLTy1nRw1uaSkK2kfjOA6a36879ka2K8pzo8xcjmI3HNGPaakwUvwsYvoWxlaD4UbX4D+6vUZWnIWpYeX8r+c9Y6FS0qNWVAihCwbxVNPasiQ/9r3WVjPbsxevFoTsWd4pMu/6HXuTDYMw+OrbPu6L3rQYthnG86kCcP/kRQ2BYmdJ/Arc1uvfiZL3iBmL1zWVWjCcvq+bHm3G4SUhOo6FGRvnX7ElB/AD08qlJ2/2LY+6c1jBqgWgtoMYzk5kPYSCJLjy9j+Ynllw7pzsZFldfq3cBNAe9dzm/nAlXl46CP+T74e4bHJ/F65S64uZe1mtTGB5NSxovNoZtZdnwZy44vIyIhIvemysWvWjcg5WtA86HW5+vbh+CoA4z8aySPt32csf6jif7jMT4KWcKcCuWpU74Or3Z7lR51elyaXgFdtYFARFyB/cAg4CSwGbhLVXfndszlBoJTcaeI3vwtrVZ+ZA33uvPHvO++isjZxLMcPHcw1/dTNZWt4VtZcmwJB6Os/Vr6tGRA/QEE1A8gKCyItza+xWNtH+OJdk8U6NyHow9Tr3y9i9XXDEtegzUfWVNRNLzYL5CclsxzK59j+YnlPOL3MPP2zcY9IZqZp8Op1PMZ6P2MNd11dsnxsPI9axheOR9iAiawd+c064Go69+HKk2y7O7q4op/Vf9L85WbtBSCfx3FffG7aF+2Bl/d/g9uuRyr859h/87pLO03jqURWy5eCH1aEFA/gLbV2uIqOV9YUyIPsmnfbyyL3s9Rexf/dHcGVmtPgP+D1K/fK+98JpyzpvTY8yccXGqNP/f0ti4Kvr2tScv2/mX1Nbm4Q8PedlAYwu6ZtzHRNYY97i70rduXl7u+TK3ytfL/bNLTIWyX1fTjemmt80TMiQs3JdvCt6Eo9dKFgJhoAqp3oErfVxi99gXOJkTyeZIXnU/ttA6s2sy6a69U3wpkh5ZDWhIJ5aowvk5d1qae4/kO47nufDzLN37C0jIubCpbllTSqVq2KgPqDaBNtTZsDt3MihMriEmOwdPVkx61exDQIIC+FZrgRjqr40+y7NgyVp1axfmU85RzK0efun3oX69/zs/rACTHUt3NC9863fL/fBygqkzeMZnPt33OwPPxTIyMYnObESytUpOVJ1cSmxxLWbey9KrTiwH1BzDEdwjuLjn8/aWlQvDvVqA7sARSzkMZb2g2hPHuMayPPcpzUpXPEo8Q7erGfa0fYGy7sZR1K5zr1NUcCLoDE1V1iP36JQBVfSe3Yy67j8DudKHlCLh1ikMdY1eb4zHHs9y9ZuhdpzefB3xeeA+mpCTAF92tqn/FrBebFJRXyiTzt3saZdLT+TG9Bi2HfwHVW+Sf7ukdVn9AxsIdN30J7e4unDynp/HHb3fyn/h91EkHT825UzdWUwl3c0WQC7WsAfUHULeC4wMFND2dw0eWsmTXDyw9u5M9LtZY+jpp4EkenclpyYBaF/ky5a3aqHs5yHKMWk0PSbHWV1oyinDM3Y1K7hV4qedEBjcYnGUgQGE5k3DGurM9toSNpzeSSjqiSoX0dL4KjaBNVT+7HX84VGuW9eCkWKsTd898kg8s4gVvD5Z4lUNUURHqlatJQMPrCKgfgH81/yx/qynpKQSFBbH02FKWHV9GeEI4buKGi7iQnJ5M5TKV6V+/PwH1A+haq2vOnapF4Iftk/nvts8ulMm7jDf96vYjoH4A3Wt3x9OtAA/xpSRYT8/v+RP2/c3h1FhurlOLdBFal6nGxMFf0MLHgf+pAriaA8FtwHWq+oj9ehTQVVWfzLbfaGA0QP369TseO3YZc3oH/wEHFltNFzncGV1rwuPDWX58OQeiDvCv9v/KvR39cp3eblVjc3ioLE2V71LDaF6nG336TMzS3pmv9DRrgrT0VOg2tvDyC6DK9L/HEHQ292l+3Vzc6OJ/H/2aDM/9jrKAQkICWbb9W7ZF7iY9r/8hDy8rsHpWJq94kUViDMSepq57BR6+7mu8PQv595yLmOQYVu/9jZ17f+OWGj1o1u4+8HYwWKYmkXp4BVN3fENK+eoEdHmKppWbORS80jWdXWd2sez4MlLSU+hfrz/tq7fPu/mrCC1a9x47IoPp3ekJOtbomGffnsPSUuH4OmZv+5p073rc3u8tp5T3ag4EtwNDsgWCLqr6r9yOKYzho4ZhGKWNo4GgOKaYOAlkHvdWFzCrghiGYRST4ggEm4GmItJQRDyAkcC8YsiHYRiGQTHMPqqqqSLyJLAQa/jod6oaXNT5MAzDMCzF0oOqqn8DfxfHuQ3DMIysSvU01IZhGIYJBIZhGKWeCQSGYRilnAkEhmEYpdw1MfuoiEQAl/FoMQBVgTOFmJ1rgSlz6WDKXDpcSZkbqGq+8+1fE4HgSohIoCNP1pUkpsylgylz6VAUZTZNQ4ZhGKWcCQSGYRilXGkIBJOLOwPFwJS5dDBlLh2cXuYS30dgGIZh5K001AgMwzCMPJhAYBiGUcqV6EAgIteJyD4ROSgiLxZ3fgpCROqJyHIR2SMiwSLylL3dR0QWi8gB+3vlTMe8ZJd1n4gMybS9o4jstN+bJPayUSJSRkRm2ds3iohvkRc0GxFxFZGtIjLffl2iywsgIpVE5FcR2Wv/vruX5HKLyHj7b3qXiPwsIp4lsbwi8p2IhIvIrkzbiqScInK/fY4DInJ/vplV1RL5hTXF9SGgEeABbAdaFXe+CpD/WkAH++cKwH6gFfA+8KK9/UXgPfvnVnYZywAN7bK72u9tArpjLZa4ALje3v448JX980hg1lVQ7meAGcB8+3WJLq+dl2nAI/bPHkClklpuoA5wBChrv54NPFASywv0AToAuzJtc3o5AR/gsP29sv1z5TzzWtz/BE78JXQHFmZ6/RLwUnHn6wrKMxcYBOwDatnbagH7ciof1noP3e199mbafhfwdeZ97J/dsJ5elGIsY11gKTCAi4GgxJbXzkdFrAujZNteIsuNFQhO2BcpN2A+MLgEl9eXrIHA6eXMvI/93tfAXXnlsyQ3DWX8wWU4aW+75thVvvbARqCGqp4GsL9Xt3fLrbx17J+zb89yjKqmAtFAFacUwjGfAM8D6Zm2leTyglVjjQC+t5vEvhURL0pouVX1FPABcBw4DUSr6iJKaHlzUBTlLPC1ryQHAslh2zU3VlZEygO/AU+rakxeu+awTfPYntcxRU5EhgHhqhrk6CE5bLtmypuJG1bzwZeq2h44j9VkkJtrutx2m/iNWM0ftQEvEbk3r0Ny2HbNlLcACrOcBS5/SQ4EJ4F6mV7XBUKKKS+XRUTcsYLAdFWdY28OE5Fa9vu1gHB7e27lPWn/nH17lmNExA3wBs4Wfkkc0hMYISJHgZnAABH5iZJb3gwngZOqutF+/StWYCip5R4IHFHVCFVNAeYAPSi55c2uKMpZ4GtfSQ4Em4GmItJQRDywOlPmFXOeHGaPDJgC7FHVjzK9NQ/IGAVwP1bfQcb2kfZIgoZAU2CTXf2MFZFudpr3ZTsmI63bgGVqNyoWNVV9SVXrqqov1u9qmareSwktbwZVDQVOiEhze1MAsJuSW+7jQDcRKWfnMwDYQ8ktb3ZFUc6FwGARqWzXwAbb23JXHB0oRdhRMxRrtM0h4OXizk8B894Lqzq3A9hmfw3FagNcChywv/tkOuZlu6z7sEcW2Ns7Abvs9z7n4hPlnsAvwEGskQmNirvcdr76cbGzuDSUtx0QaP+u/8Aa6VFiyw28Buy18/oj1kiZElde4GesfpAUrLv0h4uqnMBD9vaDwIP55dVMMWEYhlHKleSmIcMwDMMBJhAYhmGUciYQGIZhlHImEBiGYZRyJhAYhmGUciYQGE4hIioiH2Z6/ZyITHTCeX4WkR0iMj7TtpdFZJv9lZbp53EOpvmtiLTKZ5/HROS+K82/ndYKe8bJjHz+ms/+nURkUiGcd6qI3Hal6RjXPjN81HAKEUnEGkPdWVXPiMhzQHlVnViI56gJbFTVBnnsE6eq5bNtE6y//fRcDitSIrICeE5VA4v4vFOxntfIM/AYJZ+pERjOkoq11ur47G+ISAMRWWrfyS8Vkfp5JSTWfPXf23OybxWR/vZbi4Dq9l1073zS8BVrrv8vgC1APRH5UkQCxZob/7VM+64QkU72z3Ei8paIbBeRDSJSw94+0Q5uGfu/JyKbRGR/Rl7sp2dn2+WcJdac8Z0c/QDtO/avRGS1ne4we3s/ubheQ99MNYmtIlJBLP8Va77/nSJyp72viMjnIrJbRP7i4oRnGXPerxSRIBFZKBenQRhn779DRGY6mnfj2mICgeFM/wPuERHvbNs/B35QVX9gOpBfM8cTAKraBmuK3Wki4gmMAA6pajtVXe1Afprb522vqsewnjbvBPgDfUXEP4djvIANqtoWWAU8mkvabqraBXgamGBvexw4Z5fzDaBjHnmbnumC/t9M232BvsANwFd2uTN7DnhCVdsBvYEE4Basp5XbYs3t81/7wn6z/Rm0scvRAy7MafUZcJuqdgS+A96y038RaG+X4bE88m9cw9yKOwNGyaWqMSLyAzAO6wKVoTvWxQqsKQbezyepXlgXKlR1r4gcA5oBec3GmpNjqroh0+s7RGQ01v9BLazFQXZkOyYZa858gCCsNSFyMifTPr6Z8v2pne9dIpI97czuyaVpaLbdhHVARA4DLbK9vxb4SESmA3NU9aSI9AJ+VtU0rEnOVgKdsRZKydgeIiLL7DSaA37AYqvVDFesZj2wPo/pIvIH1vQXRglkagSGs32CNceKVx775NdRldO0upfj/IUErYm9ngMC7Lvdv7DmbskuRS92pKWR+81TUg77FEa+s382WV6r6rvAI0BZYIOItMjnvDl91gIE2zWrdqraRlUH2+/dgFWz6wgEiTXLpVHCmEBgOJWqnsVajvDhTJvXYc0wCnAPsCafZFbZ+yEizYD6WBNzXYmKWIEh2m73v/4K08vJGuAOAHsUUpvLSON2EXERkcZYi9hkKbeINFbVnar6HtbEdS2wPq87xVr/uRpWTWCTvX2kvb0WkNHXsg+oJiLd7TTdRaS1iLgA9VR1OdaCQZWALB3vRslgortRFD4Ensz0ehzwnYj8H9bqXA+CNSQTQFW/ynb8F1jt4zuxOqEfUNUkuxnjsqjqdhHZCgRjrem69rITy90XWP0ZO4CtWM0s0bnsO11EMprPzqjqQPvnfcBKoAbwmKomZiv303bneRrW9NULsJqzumOtgavA86oaKiK/Yy0DuhNrVt6VAKqaLNYw0kl2f44bVk1uP/CTvU2Aj1U16go+D+MqZYaPGoaTiIgr4G5fvBtjTTvcTFWTHTx+KmZ4p1EETI3AMJynHLDcHpUjwFhHg4BhFCVTIzAMwyjlTGexYRhGKWcCgWEYRilnAoFhGEYpZwKBYRhGKWcCgWEYRin3/9D3sI1zNIA5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training  100001  episodes done in 9077.27 seconds\n"
     ]
    }
   ],
   "source": [
    "#double DQN, upgrade=True\n",
    "Q_network_d_up, Q_target_d_up, info_d_up = gen_train(double=True, upgrade=True, num_episodes=num_episodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9543a759",
   "metadata": {},
   "source": [
    "## 8.b Save Q network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "922ee6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(Q_network_d_up.state_dict(), 'Q_network_v.ckpt')\n",
    "torch.save(Q_network_d_up.state_dict(), 'Q_network_v_up.ckpt')\n",
    "torch.save(Q_network_d_up.state_dict(), 'Q_network_d.ckpt')\n",
    "torch.save(Q_network_d_up.state_dict(), 'Q_network_d_up.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c56b15",
   "metadata": {},
   "source": [
    "## 9. Let's play Connect4 with DQN agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1bec733d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U0Uuxt43Z+9soDBQ4ftti9i7dOR1Dj8vZ8nG2fJytWk2FJekE4APAtXVNk+mfU6a1VBy83loq0j+nPB1Dj8vZ8nG2fJytWq0zrKuBy4GB0R4gaZ2kzZI2v0L/5FJpcpvXlbPl42z5OFuVcQtL0oXAnoh4ZKzHRcT6iFgREStm0TKpUC2lIofajzT1ofYyLQeKY2wxfZwtH2fLx9mq1TLDOhe4SNKzwK3AeyTdXM9Q7S8cS/m4PspzDzJQGGD3sh4WbF9YzyFr5mz5OFs+zlZNEVH7g6XzgS9HxIVjPa5d8+JsrZpUsH2nvMiONY8SChZ1d7LkgdMmtb+p5Gz5OFs+b7Rsm+I+StE74gln0xaWmb0xjVVYE3rhaETcD9w/BZnMzCbMf5pjZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZslwYZlZMpr2cwmtudyzs7vREUa1elFXoyPYNPEMy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZLiwzS4YLy8yS4cIys2Q07dvL7Dt5FzvWdBOFoGPLEjofWtroSIOcLZ/P3L+b/3ju/3hTcQaPffTERsep0szHzdmOqGmGJelZSY9L6pa0ua6JgFCw/YKtdG1YyTnXrGbX8h765pfqPWxNnC2/v35bOxsvWNjoGK/RzMfN2apN5JTwLyKiKyJW1C1N5qWOXoq9bcze30ZhoMDx2xazd+nOeg9bE2fL77xFRea1zmh0jNdo5uPmbNWacg2rf06Z1lJx8HprqUj/nHIDEx3hbK8/zXzcnK1arYUVwE8lPSJp3UgPkLRO0mZJm1+hf3KpNLnN68rZXn+a+bg5W5VaF93PjYidkt4E3CvpyYj45dAHRMR6YD1Au+bFZEK1lIocaj/S1Ifay7QcKI6xxfRxttefZj5uzlatphlWROzM/t0D3AmcVc9Q7S8cS/m4PspzDzJQGGD3sh4WbG+OxVpne/1p5uPmbNXGnWFJOhooRMSB7PL7gCvrGaoQBU7d2MXWtQ8QChZ1d9K295h6DlkzZ8vvkp/t4r9fLLPv0KucePMzfH3FcXxmaXujYzX1cXO2aooY++xN0luozKqgUnC3RMRVY23TrnlxtlZNTUJrCv6YL5sum+I+StE74grZuDOsiHgaeMeUpzIzm6CmfFmDmdlIXFhmlgwXlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmloxx3w8r106lvcBzU7S7+cC+KdrXVHO2fJwtn2bNNtW5ToqIBSPdUZfCmkqSNk/HR4vl4Wz5OFs+zZptOnP5lNDMkuHCMrNkpFBY6xsdYAzOlo+z5dOs2aYtV9OvYZmZHZbCDMvMDHBhmVlCmrawJK2RtF3SU5K+0ug8Q0m6XtIeSb9tdJahJC2W9AtJT0jaJunSRmc6TFKrpN9IejTL9s1GZxpO0gxJWyXd3egsQ0l6VtLjkrolbW50nqEkzZV0u6Qns/9359R1vGZcw5I0A9gB/CXwPPAwcHFE/K6hwTKSzgP6gJsiYnmj8xwmaSGwMCK2SJoDPAJ8uBmOmyQBR0dEn6RZwIPApRHx6wZHGyTpi8AKoD0iLmx0nsMkPQusiIime9GopBuBByLiWklHAbMjYn+9xmvWGdZZwFMR8XREvAzcCnyowZkGRcQvgd5G5xguIl6MiC3Z5QPAE0BHY1NVREVfdnVW9tU0vy0lnQB8ALi20VlSIakdOA+4DiAiXq5nWUHzFlYH0DPk+vM0yQ9eKiR1AmcAmxocZVB2ytUN7AHujYimyQZcDVwODDQ4x0gC+KmkRySta3SYId4C7AW+m51KXyvp6HoO2KyFpRFua5rfxs1OUhtwB3BZRJQaneewiHg1IrqAE4CzJDXF6bSkC4E9EfFIo7OM4tyIOBN4P/C5bEmiGcwEzgT+LSLOAA4CdV1vbtbCeh5YPOT6CcDOBmVJSrY+dAewISJ+1Og8I8lOG+4H1jQ2yaBzgYuytaJbgfdIurmxkY6IiJ3Zv3uAO6ksmTSD54Hnh8yUb6dSYHXTrIX1MPBWSUuyhbyPAT9ucKamly1sXwc8ERHfbnSeoSQtkDQ3u1wE3gs82dBQmYi4IiJOiIhOKv/Xfh4RaxscCwBJR2dPoJCdbr0PaIpnpyNiF9Aj6dTsplVAXZ/gmVnPnecVEX+S9HngHmAGcH1EbGtwrEGSvg+cD8yX9Dzw9Yi4rrGpgMpM4RPA49laEcBXI2Jj4yINWgjcmD0DXABui4imevlAkzoeuLPyu4iZwC0R8V+NjVTlC8CGbGLxNPDpeg7WlC9rMDMbSbOeEpqZvYYLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNk/D/tpVdzwCACMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The winner is computer\n"
     ]
    },
    {
     "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": [
    "playWithDQN = True\n",
    "if playWithDQN:\n",
    "    Q_network_d_up.eval()\n",
    "    test_DQN_1 = connect4()\n",
    "    test_DQN_1.play_DQN(Q_network_d_up, starter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a7ed6f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hmVkyXFhmlozSfi7hq9HP3/O3RUeY0tv+ZHXREcw8wzKzdLiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZpX17mcMj29g/dDdQo7fzXPq6Lyo60pgyZ/vAA3v452cP89p5FR5/97Ki4zQYXHyQJy7fzMElA5x5/yqWP3RW0ZHGlPlr6mzHND3DklSR9Jike1oZCCCixv6hTZzSczVLT7qOweHHGK7ubvWwTSlzNoA/WLGAe9++pOgYk+oY6mTFfatZ/tCKoqM0KPPX1Nkancgp4YeBJ1sVZLwj1R20ty2mo7IYqZ2eztUcHtk6F0NPq8zZAC44bR6LuitFx5hU5+FuFu5ahKoqOkqDMn9Nna1RU4Ul6XTgMuDmlqbJVGsHaG/rG9tub+ujWjswF0NPq8zZLJ8yf02drVGzM6wNwEeA2lR3kLRe0mZJm6uHBmcj28QRWrDP2VLmbJZPmb+mr95s0y66S3oHsDciHpV04VT3i4iNwEaArmX9MZNQlbaFjNYGxrZHawNU2hbMZJezpszZymjn2u08f87TAJx9+3l0HZpXcKKXK/PX1NkaNTPDegvwLknPAHcCF0m6rZWhuir9jNb2MVJ9gYhRBoe3ML9jVSuHbFqZs5VR/yNnsu7Gi1l348WlLCso99fU2RpNO8OKiOuB6wGyGdafRcRVrQwlVVg07wr2DN4EBL2da+msnNrKIZtW5mwAV35nN//6iyH2vVRl2W1P84k1i/nAr5fjJ/KRnpd4eP13Ge0aQSF2rNvOmz9/Ce3DHYXmKvPX1NkalfZ1WPM7VjK/Y2XRMSZV5mx3vLUcT+bJdA12c/7nLis6xqTK/DV1tmNOqLAi4gHggZYkMTObhv9rjpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZsko7fthmTXr5+/526IjTOnXvvw/io7wiuIZlpklw4VlZslwYZlZMlxYZpYMF5aZJcOFZWbJcGGZWTJcWGaWDBeWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZsko7dvLHB7Zxv6hu4EavZ3n0td9UdGRxpQ52wce2MM/P3uY186r8Pi7lxUdp8Hg4oM8cflmDi4Z4Mz7V7H8obOKjjSmzMetzM+3uc7W1AxL0jOSfixpi6TNLU0ERNTYP7SJU3quZulJ1zE4/BjD1d2tHrYpZc4G8AcrFnDv25cUHWNSHUOdrLhvNcsfWlF0lJcp63Er8/OtiGwnckr4mxGxOiLWtCxN5kh1B+1ti+moLEZqp6dzNYdHtrZ62KaUORvABafNY1F3pegYk+o83M3CXYtQVUVHeZmyHrcyP9+KyFbKNaxq7QDtbX1j2+1tfVRrB4oLNE6Zs9krT5mfb0Vka7awAviWpEclrZ/sDpLWS9osaXP10ODsJTw2Qgv2OVvKnM1eecr8fGtttmYX3d8SEbskvRb4tqRtEfG98XeIiI3ARoCuZf0xk1CVtoWM1gbGtkdrA1TaFsxkl7OmzNnKaOfa7Tx/ztMAnH37eXQdmldworSU+flWRLamZlgRsSv7ey+wCXhTK0N1VfoZre1jpPoCEaMMDm9hfseqVg7ZtDJnK6P+R85k3Y0Xs+7Gi11WOZT5+VZEtmlnWJJ6gLaIeDG7fAnwqVaGkiosmncFewZvAoLezrV0Vk5t5ZBNK3M2gCu/s5t//cUQ+16qsuy2p/nEmsV84NfL8RP5SM9LPLz+u4x2jaAQO9Zt582fv4T24Y6io5X2uJX5+VZEtmZOCU8BNkk6ev87IuK+lqYC5nesZH7HylYPk0uZs93x1nI8mSfTNdjN+Z+7rOgYkyrzcSvz822us01bWBHxFPAbc5DFzOy4SvmyBjOzybiwzCwZLiwzS4YLy8yS4cIys2S4sMwsGS4sM0uGC8vMkuHCMrNkuLDMLBkuLDNLhgvLzJLhwjKzZLiwzCwZipjRuxlPvlPpl8Czs7S7k4F9s7Sv2eZs+ThbPmXNNtu5lkfEaya7oSWFNZskbZ6LjxbLw9nycbZ8ypptLnP5lNDMkuHCMrNkpFBYG4sOcBzOlo+z5VPWbHOWq/RrWGZmR6UwwzIzA1xYZpaQ0haWpEsl/VTSdkkfLTrPeJJulbRX0k+KzjKepH5J/yLpSUlbJX246ExHSeqW9LCkH2XZ/qLoTBNJqkh6TNI9RWcZT9Izkn4saYukzUXnGU9Sn6SvStqWPe/e3NLxyriGJakC/Ay4GHgOeAR4X0Q8UWiwjKQLgEPAlyLijUXnOUrSEmBJRPxQ0knAo8DvlOG4qf5JvD0RcUhSB/Ag8OGI+H7B0cZI+lNgDbAgIt5RdJ6jJD0DrImI0r1oVNIXgX+LiJsldQLzI2KgVeOVdYb1JmB7RDwVEcPAncDlBWcaExHfA/YXnWOiiPhFRPwwu/wi8CSwtNhUdVF3KNvsyP6U5qelpNOBy4Cbi86SCkkLgAuAWwAiYriVZQXlLaylwM5x289Rkm+8VEg6Azgb+EHBUcZkp1xbgL3AtyOiNNmADcBHgFrBOSYTwLckPSppfdFhxvlV4JfA32Wn0jdL6mnlgGUtLE1yXWl+GpedpF7ga8A1EXGw6DxHRUQ1IlYDpwNvklSK02lJ7wD2RsSjRWeZwlsi4hzgt4EPZksSZdAOnAP8TUScDQwCLV1vLmthPQf0j9s+HdhVUJakZOtDXwNuj4ivF51nMtlpwwPApcUmGfMW4F3ZWtGdwEWSbis20jERsSv7ey+wifqSSRk8Bzw3bqb8VeoF1jJlLaxHgNdLel22kPde4J8KzlR62cL2LcCTEfHZovOMJ+k1kvqyy/OAtwLbCg2ViYjrI+L0iDiD+nPt/oi4quBYAEjqyX6BQna6dQlQit9OR8RuYKeks7Krfgto6S942lu587wiYlTSHwPfBCrArRGxteBYYyT9A3AhcLKk54BPRMQtxaYC6jOF3wN+nK0VAXwsIu4tLtKYJcAXs98AtwFfiYhSvXygpE4BNtV/FtEO3BER9xUbqcGHgNuzicVTwB+1crBSvqzBzGwyZT0lNDN7GReWmSXDhWVmyXBhmVkyXFhmlgwXlpklw4VlZsn4TxchXZK3c3j7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter column: (0-6)\n",
      "3\n",
      "The winner is you\n"
     ]
    },
    {
     "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": [
    "if playWithDQN:\n",
    "    Q_network_d_up.eval()\n",
    "    test_DQN_2 = connect4()\n",
    "    test_DQN_2.play_DQN(Q_network_d_up, starter=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0856c9e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}