updated notebooks

notebooks/HelloLavaGap.ipynb

@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example usage of Tempestpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sb3_contrib import MaskablePPO\n",
+    "from sb3_contrib.common.wrappers import ActionMasker\n",
+    "from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat, HumanOutputFormat\n",
+    "\n",
+    "import gymnasium as gym\n",
+    "\n",
+    "from minigrid.core.actions import Actions\n",
+    "from minigrid.core.constants import TILE_PIXELS\n",
+    "from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper\n",
+    "\n",
+    "import tempfile, datetime, shutil\n",
+    "\n",
+    "import time\n",
+    "import os\n",
+    "\n",
+    "from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname, shield_needed, shielded_evaluation, create_shield_overlay_image\n",
+    "from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback\n",
+    "\n",
+    "import os, sys\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "GRID_TO_PRISM_BINARY=os.getenv(\"M2P_BINARY\")\n",
+    "\n",
+    "def mask_fn(env: gym.Env):\n",
+    "    return env.create_action_mask()\n",
+    "\n",
+    "def nomask_fn(env: gym.Env):\n",
+    "    return [1.0] * 7\n",
+    "\n",
+    "def main():\n",
+    "    env = \"MiniGrid-LavaGapS6-v0\"\n",
+    "\n",
+    "    # TODO Change the safety specification\n",
+    "    formula = \"Pmax=? [G !AgentIsOnLava]\"\n",
+    "    value_for_training = 1.0\n",
+    "    shield_comparison =  \"absolute\"\n",
+    "    shielding = ShieldingConfig.Training\n",
+    "    \n",
+    "    logger = Logger(\"/tmp\", output_formats=[HumanOutputFormat(sys.stdout)])\n",
+    "    \n",
+    "\n",
+    "    env = gym.make(env, render_mode=\"rgb_array\")\n",
+    "    image_env = RGBImgObsWrapper(env, TILE_PIXELS)\n",
+    "    env = RGBImgObsWrapper(env, 8)\n",
+    "    env = ImgObsWrapper(env)\n",
+    "    env = MiniWrapper(env)\n",
+    "\n",
+    "    \n",
+    "    env.reset()\n",
+    "    Image.fromarray(env.render()).show()\n",
+    "        \n",
+    "    shield_handlers = dict()\n",
+    "    if shield_needed(shielding):\n",
+    "        for value in [0.0, 1.0]:\n",
+    "            shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, \"grid.txt\", \"grid.prism\", formula, shield_value=value, shield_comparison=shield_comparison, nocleanup=True, prism_file=None)\n",
+    "            env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)\n",
+    "            create_shield_overlay_image(image_env, shield_handler.create_shield())\n",
+    "            print(f\"The shield for shield_value = {value}\")\n",
+    "\n",
+    "            shield_handlers[value] = shield_handler\n",
+    "\n",
+    "\n",
+    "    if shielding == ShieldingConfig.Training:\n",
+    "        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)\n",
+    "        env = ActionMasker(env, mask_fn)\n",
+    "        print(\"Training with shield:\")\n",
+    "        create_shield_overlay_image(image_env, shield_handlers[value_for_training].create_shield())\n",
+    "    elif shielding == ShieldingConfig.Disabled:\n",
+    "        env = ActionMasker(env, nomask_fn)\n",
+    "    else:\n",
+    "        assert(False) \n",
+    "    model = MaskablePPO(\"CnnPolicy\", env, verbose=1, device=\"auto\")\n",
+    "    model.set_logger(logger)\n",
+    "    steps = 20_000\n",
+    "\n",
+    " \n",
+    "    model.learn(steps,callback=[InfoCallback()])\n",
+    "\n",
+    "\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    print(\"Starting the training\")\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}