Generate solved.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "97a6f423",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym_sokoban.envs\n",
    "from gym_sokoban.envs import SokobanEnv\n",
    "from gym.spaces import Discrete, Box\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from collections import deque\n",
    "from copy import deepcopy\n",
    "from PIL import Image\n",
    "from numpy import asarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "62513b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('./gym_sokoban/envs/surface/box_on_target.png')\n",
    "solved = asarray(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4d3236a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('./gym_sokoban/envs/surface/floor.png')\n",
    "field = asarray(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "392a5793",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('./gym_sokoban/envs/surface/box_target.png')\n",
    "target = asarray(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "62240421",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open('./gym_sokoban/envs/surface/box.png')\n",
    "box = asarray(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7a5af34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = SokobanEnv((6, 6), 50, 2)\n",
    "solved_fields = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "22b313ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Not enough free spots (#3) to place 1 player and 4 boxes.\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n",
      "[SOKOBAN] Runtime Error/Warning: Generated Model with score == 0\n",
      "[SOKOBAN] Retry . . .\n"
     ]
    }
   ],
   "source": [
    "for _ in range(3431):\n",
    "    env.reset()\n",
    "    field = env.render('rgb_array')\n",
    "    for i in range(0, 160, 16):\n",
    "        for j in range(0, 160, 16):\n",
    "            if (field[i:i+16, j:j+16, :] == box).all():\n",
    "                field[i:i+16, j:j+16, :] = empty\n",
    "            elif (field[i:i+16, j:j+16, :] == target).all():\n",
    "                field[i:i+16, j:j+16, :] = solved\n",
    "    solved_fields.append(deepcopy(field))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4c11d41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solved_fields = np.array(solved_fields)\n",
    "np.save(\"solved_state\", solved_fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0457ec48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13848e740>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(solved_fields[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8593da36",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}