test_wcst_session_gym.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import gymnasium as gym\n",
    "from gymnasium import spaces\n",
    "import wcst_gym\n",
    "from wcst_gym.n_hot_observation import NHotObservation\n",
    "from wcst_session import WcstSession\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = WcstSession(1, 0)\n",
    "env = gym.make(\"WcstSession-v0\", wcst_session=sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0,  6, 10],\n",
       "        [ 1,  5,  9],\n",
       "        [ 3,  4,  8],\n",
       "        [ 2,  7, 11]]),\n",
       " {'current_rule': 10})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.reset(seed=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.observation_space.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1,  6,  9],\n",
       "        [ 3,  5, 10],\n",
       "        [ 0,  4, 11],\n",
       "        [ 2,  7,  8]]),\n",
       " 0,\n",
       " False,\n",
       " False,\n",
       " {'current_rule': 10})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 3,  6,  9],\n",
       "        [ 2,  4, 10],\n",
       "        [ 1,  5,  8],\n",
       "        [ 0,  7, 11]]),\n",
       " 1,\n",
       " False,\n",
       " False,\n",
       " {'current_rule': 10})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 2,  5, 10],\n",
       "        [ 1,  6,  8],\n",
       "        [ 0,  7,  9],\n",
       "        [ 3,  4, 11]]),\n",
       " 0,\n",
       " False,\n",
       " False,\n",
       " {'current_rule': 10})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the N-hot observation wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = NHotObservation(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0],\n",
       "        [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1],\n",
       "        [1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n",
       "        [0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]], dtype=int8),\n",
       " {'current_rule': 11})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the N-hot observation wrapper interactions with VectorEnvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sess = WcstSession(\n",
    "    correct_value=1, \n",
    "    incorrect_value=0, \n",
    ")\n",
    "def make_wcst():\n",
    "    env = gym.make(\"WcstSession-v0\", wcst_session=test_sess)\n",
    "    env = NHotObservation(env)\n",
    "    return env\n",
    "envs = gym.vector.SyncVectorEnv([make_wcst])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n",
       "         [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
       "         [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n",
       "         [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0]]], dtype=int8),\n",
       " {'current_rule': array([3]), '_current_rule': array([ True])})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "envs.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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