{ "cells": [ { "cell_type": "markdown", "id": "72b3fb19-0af4-4bc8-bc22-32e1a0c8057f", "metadata": {}, "source": [ "# Gymnasium integration\n", "[Gymnasium](https://gymnasium.farama.org/) is an API standard for reinforcement learning with a diverse collection of reference environments. Stormvogel supports some integration with gymnasium. In particular, you can construct explicit models from the gymnasium environmnents under Gymnasium's [ToyText](https://gymnasium.farama.org/environments/toy_text/) (except Blackjack). " ] }, { "cell_type": "markdown", "id": "27be6901-4d8c-407b-af77-028ff0042042", "metadata": {}, "source": [ "## FrozenLake\n", "Let us create one of these environments, called FrozenLake.\n", "Our agent wants to get to the present. Currently, it just chooses a random action." ] }, { "cell_type": "code", "execution_count": 1, "id": "88910590-1351-4842-90f5-f0c137622a41", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:39.174050Z", "iopub.status.busy": "2025-07-21T08:29:39.173887Z", "iopub.status.idle": "2025-07-21T08:29:40.460832Z", "shell.execute_reply": "2025-07-21T08:29:40.460386Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import gymnasium as gym\n", "from stormvogel.extensions.gym_grid import *\n", "from stormvogel import *\n", "import IPython.display as ipd\n", "\n", "env = gym.make(\"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=False) # Set `is_slippery=True` for stochastic behavior\n", "filename = gymnasium_render_model_gif(env, filename=\"ice1\")\n", "extensions.embed_gif(filename)" ] }, { "cell_type": "markdown", "id": "8e41a630-2f18-43e4-a1d2-8d2f6dae9a19", "metadata": {}, "source": [ "We can convert it into an explicit MDP as follows. Each state has a label that relates to the coordinates of the tile." ] }, { "cell_type": "code", "execution_count": 2, "id": "29ca2085-5f8f-4b7c-9f42-1303b3a0e48a", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:40.462603Z", "iopub.status.busy": "2025-07-21T08:29:40.462330Z", "iopub.status.idle": "2025-07-21T08:29:42.540877Z", "shell.execute_reply": "2025-07-21T08:29:42.540349Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3e0c7e0fe8da4af0aa1aa22e311fd9a8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "function return_id_result(url, id, data) {\n", " fetch(url, {\n", " method: 'POST',\n", " body: JSON.stringify({\n", " 'id': id,\n", " 'data': data\n", " })\n", " })\n", " }\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "return_id_result('http://127.0.0.1:8889', 'wxslhECwBctIzxhcvqAx', 'test message')" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Test request failed. See 'Implementation details' in docs. Disable warning by use_server=False.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "479fa6c06e644a5d9a0e4d2378ab113e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", " \n", " Network\n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sv_model = gymnasium_grid_to_stormvogel(env, GRID_ACTION_LABEL_MAP)\n", "# GRID_ACTION_LABEL_MAP = {0: \"←\", 1: \"↓\", 2: \"→\", 3: \"↑\", 4: \"pickup\", 5: \"dropoff\"} (map between gymnasium action ids and labels)\n", "vis = show(sv_model, layout=Layout(\"layouts/frozenlake.json\"))" ] }, { "cell_type": "markdown", "id": "9808734d-b750-46b4-bfb6-423e516ce257", "metadata": {}, "source": [ "Now, let's do some model checking to calculate a strategy to solve the puzzle. We will tell the model checker to maximize the probability of getting to the target state (the present)." ] }, { "cell_type": "code", "execution_count": 3, "id": "2db709fa-5735-4fd2-9b0d-e9c36c8f3ff2", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:42.590190Z", "iopub.status.busy": "2025-07-21T08:29:42.590019Z", "iopub.status.idle": "2025-07-21T08:29:42.679625Z", "shell.execute_reply": "2025-07-21T08:29:42.679090Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cd8c264fb4074221a028e6c2858e27db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "607da66cf8494b8382e656a6350e8b47", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", " \n", " Network\n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res = model_checking(sv_model, f'Pmax=? [F \"target\"]')\n", "vis2 = show(sv_model, result=res, layout=Layout(\"layouts/frozenlake.json\"))" ] }, { "cell_type": "markdown", "id": "c3eabd69-5579-47ca-b121-aa9244d53976", "metadata": {}, "source": [ "Let's highlight the path to see what the scheduler is doing." ] }, { "cell_type": "code", "execution_count": 4, "id": "6d45cc94-244a-4d67-b32a-65762672507a", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:42.729865Z", "iopub.status.busy": "2025-07-21T08:29:42.729536Z", "iopub.status.idle": "2025-07-21T08:29:55.773900Z", "shell.execute_reply": "2025-07-21T08:29:55.773395Z" } }, "outputs": [], "source": [ "path = simulate_path(sv_model, scheduler=res.scheduler, steps=20)\n", "vis2.highlight_path(path, color=\"orange\")" ] }, { "cell_type": "markdown", "id": "e2caeb6c-294a-4707-abc0-a07907506c79", "metadata": {}, "source": [ "Alternatively, we can show what our scheduler does in the frozen lake environment itself." ] }, { "cell_type": "code", "execution_count": 5, "id": "28240a9f-c0fa-4ba5-9192-3ea47aab9a0d", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:55.775759Z", "iopub.status.busy": "2025-07-21T08:29:55.775442Z", "iopub.status.idle": "2025-07-21T08:29:55.821397Z", "shell.execute_reply": "2025-07-21T08:29:55.820887Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from stormvogel.extensions.gym_grid import *\n", "gs = to_gymnasium_scheduler(sv_model, res.scheduler, GRID_ACTION_LABEL_MAP)\n", "filename = gymnasium_render_model_gif(env, gs, filename=\"ice2\")\n", "extensions.embed_gif(filename)" ] }, { "cell_type": "markdown", "id": "1a4715dc-d299-45da-bc69-c0291c9d448f", "metadata": {}, "source": [ "We can also define a function to act as the scheduler on the model and convert it to a gymnasium scheduler. This one just keeps going in a loop..." ] }, { "cell_type": "code", "execution_count": 6, "id": "239c5130-8d51-4a68-bebe-f86b06430f55", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:55.823347Z", "iopub.status.busy": "2025-07-21T08:29:55.822949Z", "iopub.status.idle": "2025-07-21T08:29:56.127431Z", "shell.execute_reply": "2025-07-21T08:29:56.126997Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from stormvogel.model import Action\n", "\n", "def my_scheduler(s: stormvogel.model.State):\n", " # \"←\" \"↓\" \"→\" \"↑\"\n", " if s.is_initial():\n", " return Action.create(\"→\")\n", " env_id = int(s.labels[0])\n", " x,y = to_coordinate(env_id,env)\n", " if x < 2 and y == 0:\n", " return Action.create(\"→\")\n", " elif x == 2 and y < 2:\n", " return Action.create(\"↓\")\n", " elif x > 0 and y == 2:\n", " return Action.create(\"←\")\n", " else:\n", " return Action.create(\"↑\")\n", "\n", "gs = to_gymnasium_scheduler(sv_model, my_scheduler, GRID_ACTION_LABEL_MAP)\n", "filename = gymnasium_render_model_gif(env, gs, filename=\"ice3\")\n", "extensions.embed_gif(filename)" ] }, { "cell_type": "markdown", "id": "5260e1e3-9b6b-4ae6-b48e-276477ad4059", "metadata": {}, "source": [ "## CliffWalking\n", "CliffWalking is a slightly more boring version of FrozenLake. You can apply the same principles that we just applied to FrozenLake." ] }, { "cell_type": "code", "execution_count": 7, "id": "4ad97870-e088-46d8-86b1-011f82e4c6e9", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:56.129020Z", "iopub.status.busy": "2025-07-21T08:29:56.128853Z", "iopub.status.idle": "2025-07-21T08:29:56.134177Z", "shell.execute_reply": "2025-07-21T08:29:56.133799Z" } }, "outputs": [], "source": [ "import gymnasium as gym\n", "from stormvogel.extensions.gym_grid import *\n", "\n", "env = gym.make(\"CliffWalking-v0\", render_mode=\"rgb_array\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "c7e7c0b1-1dbe-42a0-a0a8-da742cffc03a", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:56.135691Z", "iopub.status.busy": "2025-07-21T08:29:56.135530Z", "iopub.status.idle": "2025-07-21T08:29:56.203936Z", "shell.execute_reply": "2025-07-21T08:29:56.203417Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aaa138841adc485f9fb76cdbeabd9688", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79146f6aeb9d4694930182c73902def0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", " \n", " Network\n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sv_model = gymnasium_grid_to_stormvogel(env, GRID_ACTION_LABEL_MAP)\n", "vis = show(sv_model, layout=Layout(\"layouts/cliffwalking.json\"))" ] }, { "cell_type": "code", "execution_count": 9, "id": "b5c3cd5a-aeb5-492c-bb30-ec37375be9ce", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:56.253394Z", "iopub.status.busy": "2025-07-21T08:29:56.253187Z", "iopub.status.idle": "2025-07-21T08:29:56.470851Z", "shell.execute_reply": "2025-07-21T08:29:56.470412Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from stormvogel.stormpy_utils.model_checking import model_checking\n", "res = model_checking(sv_model, f'Pmax=? [F \"target\"]')\n", "gs = to_gymnasium_scheduler(sv_model, res.scheduler, GRID_ACTION_LABEL_MAP)\n", "filename = gymnasium_render_model_gif(env, gs, filename=\"cliff\")\n", "extensions.embed_gif(filename)" ] }, { "cell_type": "markdown", "id": "4192e230-efb6-4fb4-a818-da147ca7de77", "metadata": {}, "source": [ "## Taxi\n", "In the Taxi scenario, a taxi has to pick up passengers and transport them to the hotel. The position of the target, passenger and taxi are chosen at random." ] }, { "cell_type": "code", "execution_count": 10, "id": "813a8e52-e176-4d39-a92f-3a77a40ae484", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:56.472508Z", "iopub.status.busy": "2025-07-21T08:29:56.472336Z", "iopub.status.idle": "2025-07-21T08:29:56.538413Z", "shell.execute_reply": "2025-07-21T08:29:56.537858Z" } }, "outputs": [ { "data": { "text/plain": [ "'ModelType.MDP model with name None, 505 states, 7 actions, and 1005 distinct labels.'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gymnasium as gym\n", "from stormvogel.extensions.gym_grid import *\n", "env = gym.make(\"Taxi-v3\", render_mode=\"rgb_array\") # Set `is_slippery=True` for stochastic behavior\n", "sv_model = gymnasium_grid_to_stormvogel(env)\n", "# This model is so big that it is better not to display it.\n", "sv_model.summary()" ] }, { "cell_type": "code", "execution_count": 11, "id": "6c8f787c-697d-4f0a-a915-f6a943ac8254", "metadata": { "execution": { "iopub.execute_input": "2025-07-21T08:29:56.540116Z", "iopub.status.busy": "2025-07-21T08:29:56.539945Z", "iopub.status.idle": "2025-07-21T08:29:58.795130Z", "shell.execute_reply": "2025-07-21T08:29:58.794618Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "target = get_target_state(env)\n", "res = model_checking(sv_model, f'Rmax=? [S]')\n", "gs = to_gymnasium_scheduler(sv_model, res.scheduler, GRID_ACTION_LABEL_MAP)\n", "filename = gymnasium_render_model_gif(env, gs, filename=\"taxi\")\n", "extensions.embed_gif(filename)" ] }, { "cell_type": "code", "execution_count": null, "id": "999369d8-c9fc-4942-842f-5a82242f2cf2", "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": "3.13.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "054bce67f3744f009bb981465a8dc8ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", 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