sr-dyna-detour-task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SR-Dyna (Detour Task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import srdyna\n",
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'srdyna' from '/Users/jeremygordon/repos/sr-dyna/srdyna.py'>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(srdyna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 10x10 world with 102 states\n",
      "Trial finished in 135 steps\n",
      "Trial finished in 37 steps\n",
      "Trial finished in 31 steps\n",
      "Trial finished in 12 steps\n",
      "Trial finished in 12 steps\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Detour Task\n",
    "REPLAY = \"sufficient\"\n",
    "EXPLORE_STEPS = 10000\n",
    "POST_REWARD_TRIALS = 5\n",
    "WALL_LEARNING_STEPS = 100  # 40\n",
    "REPLAY_STEPS = {\n",
    "    \"insufficient\": 10,\n",
    "    \"sufficient\": 25000\n",
    "}[REPLAY]\n",
    "env = srdyna.SimpleGridWorld(world='worlds/detour_task.txt')\n",
    "S_LOC = (0, 5)\n",
    "agent = srdyna.SRDyna(id=0, loc=S_LOC, env=env)\n",
    "\n",
    "# Explore\n",
    "for i in range(EXPLORE_STEPS):\n",
    "    agent.step(random_policy=True)\n",
    "\n",
    "# Add reward\n",
    "R_LOC = (9, 5)\n",
    "env.add_reward(R_LOC, 10)\n",
    "\n",
    "for i in range(POST_REWARD_TRIALS):\n",
    "    # Repeated trials from S (until reward reached)\n",
    "    agent.terminate_episode(reset_state=env.state_at_loc(S_LOC))\n",
    "    done = False\n",
    "    steps = 0\n",
    "    MAX_STEPS = 500\n",
    "    while not done and steps < MAX_STEPS:\n",
    "        done = agent.step(verbose=False)\n",
    "        steps += 1\n",
    "    print(\"Trial finished in %d steps\" % steps)\n",
    "\n",
    "# One-step replay samples from random sa's\n",
    "agent.learn_offline(k=REPLAY_STEPS)\n",
    "    \n",
    "agent.make_plots(sr_state=env.state_at_loc((4, 5)))\n",
    "\n",
    "# Add barrier\n",
    "B_LOC = (5, 5)\n",
    "S2_LOC = (4, 5)\n",
    "env.wall_coords.append(B_LOC)\n",
    "env.map = env.get_map()\n",
    "    \n",
    "for i in range(WALL_LEARNING_STEPS):\n",
    "    # One-step runs from left of new wall\n",
    "    reset_state = env.state_at_loc(S2_LOC)\n",
    "    agent.terminate_episode(reset_state=reset_state)\n",
    "    agent.step()\n",
    "\n",
    "agent.make_plots(sr_state=env.state_at_loc((4, 5)))\n",
    "    \n",
    "# One-step replay samples from random sa's\n",
    "agent.learn_offline(k=REPLAY_STEPS)\n",
    "    \n",
    "agent.make_plots(sr_state=env.state_at_loc((4, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate anim (slow)\n",
    "agent.record_trials(title=\"detour\", \n",
    "                    learning=False,\n",
    "                    start_locs=[(0, 5), (4, 5), (7, 0), (7, 7)])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}