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{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Example how to combine shielding with stable baselines contrib maskable ppo algorithm." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sb3_contrib import MaskablePPO\n", "from sb3_contrib.common.maskable.evaluation import evaluate_policy\n", "from sb3_contrib.common.maskable.policies import MaskableActorCriticPolicy\n", "from sb3_contrib.common.wrappers import ActionMasker\n", "from stable_baselines3.common.callbacks import BaseCallback\n", "\n", "import gymnasium as gym\n", "\n", "from shieldhandlers import MiniGridShieldHandler, create_shield_query\n", "from wrappers import MiniGridSbShieldingWrapper" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def mask_fn(env: gym.Env):\n", " return env.create_action_mask()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "shield_creator = MiniGridShieldHandler(\"grid.txt\", \"./main\", \"grid.prism\", \"Pmax=? [G !\\\"AgentIsInLavaAndNotDone\\\"]\")\n", "\n", "env = gym.make(\"MiniGrid-LavaCrossingS9N1-v0\", render_mode=\"rgb_array\")\n", "env = MiniGridSbShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query, mask_actions=True)\n", "env = ActionMasker(env, mask_fn)\n", "model = MaskablePPO(MaskableActorCriticPolicy, env, verbose=1)\n", "\n", "\n", "model.learn(10_000)" ] } ], "metadata": { "kernelspec": { "display_name": "env", "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" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2}
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