sr-dyna-novel-revaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SR-Dyna (Novel Revaluation Task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import srdyna\n",
    "import importlib\n",
    "import matplotlib.animation as manimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'srdyna' from '/Users/jeremygordon/repos/sr-dyna/srdyna.py'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(srdyna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 10x10 world with 103 states\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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Latent Learning\n",
    "REPLAY = \"sufficient\"\n",
    "EXPLORE_STEPS = 25000\n",
    "S1_S2_TRIALS = 20\n",
    "POST_REWARD_STEPS = 40\n",
    "REPLAY_STEPS = {\n",
    "    \"insufficient\": 10,\n",
    "    \"sufficient\": 10000\n",
    "}[REPLAY]\n",
    "S1_LOC = (0, 2)\n",
    "S2_LOC = (8, 1)\n",
    "env = srdyna.SimpleGridWorld(world='worlds/latent_learning.txt', max_reward_locs=2)\n",
    "agent = srdyna.SRDyna(id=0, loc=(0, 2), env=env)\n",
    "\n",
    "R1_LOC = (9, 8)\n",
    "R2_LOC = (9, 1)\n",
    "env.add_reward(R1_LOC, 0)  # Initially empty, but terminates\n",
    "env.add_reward(R2_LOC, 0)  # Initially empty, but terminates\n",
    "\n",
    "# Explore\n",
    "for i in range(EXPLORE_STEPS):\n",
    "    agent.step(random_policy=True)\n",
    "\n",
    "# Add reward\n",
    "env.reward_locs[R1_LOC]['reward'] = 10  \n",
    "\n",
    "for i in range(POST_REWARD_STEPS):\n",
    "    # Repeated one-step runs from R1\n",
    "    agent.terminate_episode(reset_state=env.state_at_loc(R1_LOC))\n",
    "    agent.step(verbose=False)\n",
    "\n",
    "# One-step replay samples from random sa's\n",
    "agent.learn_offline(k=REPLAY_STEPS)\n",
    "    \n",
    "agent.make_plots(sr_state=9)\n",
    "\n",
    "# Trials from S1 or S2 (until R1 reached)\n",
    "for i in range(S1_S2_TRIALS):\n",
    "    s_loc = S1_LOC if i % 2 == 0 else S2_LOC\n",
    "    agent.terminate_episode(reset_state=env.state_at_loc(s_loc))\n",
    "    done = False\n",
    "    steps = 0\n",
    "    MAX_STEPS = 1000\n",
    "    while not done and steps < MAX_STEPS:\n",
    "        done = agent.step(verbose=False)\n",
    "        steps += 1\n",
    "        \n",
    "# Increase second reward\n",
    "env.reward_locs[R2_LOC]['reward'] = 20  \n",
    "\n",
    "# Learn about R2 - repeated one-step runs from R2\n",
    "for i in range(POST_REWARD_STEPS):\n",
    "    agent.terminate_episode(reset_state=env.state_at_loc(R2_LOC))\n",
    "    agent.step(verbose=False)\n",
    "\n",
    "agent.make_plots(sr_state=9)\n",
    "\n",
    "# One-step replay samples from random sa's\n",
    "agent.learn_offline(k=REPLAY_STEPS)\n",
    "    \n",
    "agent.make_plots(sr_state=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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=\"novel_revaluation\", \n",
    "                    learning=False,\n",
    "                    start_locs=[(0, 2), (4, 0), (9, 0), (0, 8)])"
   ]
  }
 ],
 "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
}