ExampleUsage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This section uses the multiworld environment API\n",
    "https://github.com/vitchyr/multiworld/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from crafting_env.multiworld_crafting_env import MultiworldCraftingEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "asset_path /home/coline/arxiv_cpv/craftingworld/crafting_env/assets/*.png\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env = MultiworldCraftingEnv(pretty_renderable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9732aa6748>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "a = np.random.randint(6)\n",
    "print(a)\n",
    "obs,r,d,info  = env.step(a)\n",
    "obs = obs['observation']\n",
    "plt.imshow(obs.reshape(33,30,3)[:,:,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9732571fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "obs = env.pretty_render()\n",
    "plt.imshow(obs.reshape(33*10,30*10,3)[:,:,::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This section uses the GymEnv API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from crafting_env.gym_crafting_env import GymCraftingEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env = CraftingGymEnv(res=3)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f97324deba8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "a = np.random.randint(6)\n",
    "print(a)\n",
    "obs,r,d,info  = env.step(a)\n",
    "plt.imshow(obs.reshape(33,30,3)[:,:,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}