{
"cells": [
{
"cell_type": "markdown",
"id": "74a94a47",
"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": "72cff06c",
"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": "22c4643c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:07.790656Z",
"iopub.status.busy": "2026-03-26T10:48:07.790420Z",
"iopub.status.idle": "2026-03-26T10:48:08.214081Z",
"shell.execute_reply": "2026-03-26T10:48:08.213495Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/__w/stormvogel/stormvogel/.venv/lib/python3.12/site-packages/pygame/pkgdata.py:25: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
" from pkg_resources import resource_stream, resource_exists\n"
]
},
{
"data": {
"text/html": [
"![](\"gifs/ice1.gif\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import gymnasium as gym\n",
"from stormvogel.extensions.gym_grid import *\n",
"from stormvogel import *\n",
"\n",
"env = gym.make(\n",
" \"FrozenLake-v1\", render_mode=\"rgb_array\", is_slippery=False\n",
") # 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": "1319f4fb",
"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": "35aa00f9",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:08.215956Z",
"iopub.status.busy": "2026-03-26T10:48:08.215727Z",
"iopub.status.idle": "2026-03-26T10:48:08.400455Z",
"shell.execute_reply": "2026-03-26T10:48:08.399885Z"
}
},
"outputs": [
{
"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": "27def322",
"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": "aca167ce",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:08.407750Z",
"iopub.status.busy": "2026-03-26T10:48:08.407447Z",
"iopub.status.idle": "2026-03-26T10:48:08.441734Z",
"shell.execute_reply": "2026-03-26T10:48:08.441150Z"
}
},
"outputs": [
{
"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, 'Pmax=? [F \"target\"]')\n",
"vis2 = show(sv_model, result=res, layout=Layout(\"layouts/frozenlake.json\"))"
]
},
{
"cell_type": "markdown",
"id": "81862769",
"metadata": {},
"source": [
"Let's highlight the path to see what the scheduler is doing."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "48809076",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:08.448556Z",
"iopub.status.busy": "2026-03-26T10:48:08.448383Z",
"iopub.status.idle": "2026-03-26T10:48:21.494583Z",
"shell.execute_reply": "2026-03-26T10:48:21.493995Z"
}
},
"outputs": [],
"source": [
"path = simulate_path(sv_model, scheduler=res.scheduler, steps=20)\n",
"vis2.highlight_path(path, color=\"orange\")"
]
},
{
"cell_type": "markdown",
"id": "b817a32d",
"metadata": {},
"source": [
"Alternatively, we can show what our scheduler does in the frozen lake environment itself."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4f40ffaa",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:21.496419Z",
"iopub.status.busy": "2026-03-26T10:48:21.496228Z",
"iopub.status.idle": "2026-03-26T10:48:21.542383Z",
"shell.execute_reply": "2026-03-26T10:48:21.541927Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"![](\"gifs/ice2.gif\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from stormvogel.extensions.gym_grid import *\n",
"\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": "0fcb9021",
"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": "ada5c535",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:21.544099Z",
"iopub.status.busy": "2026-03-26T10:48:21.543920Z",
"iopub.status.idle": "2026-03-26T10:48:21.848022Z",
"shell.execute_reply": "2026-03-26T10:48:21.847521Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"![](\"gifs/ice3.gif\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from stormvogel.model import Action\n",
"\n",
"\n",
"def my_scheduler(s: stormvogel.model.State):\n",
" # \"←\" \"↓\" \"→\" \"↑\"\n",
" if s.is_initial():\n",
" return Action(\"→\")\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(\"→\")\n",
" elif x == 2 and y < 2:\n",
" return Action(\"↓\")\n",
" elif x > 0 and y == 2:\n",
" return Action(\"←\")\n",
" else:\n",
" return Action(\"↑\")\n",
"\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": "84a92ebf",
"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": "75433916",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:21.849740Z",
"iopub.status.busy": "2026-03-26T10:48:21.849576Z",
"iopub.status.idle": "2026-03-26T10:48:21.855230Z",
"shell.execute_reply": "2026-03-26T10:48:21.854692Z"
}
},
"outputs": [],
"source": [
"import gymnasium as gym\n",
"from stormvogel.extensions.gym_grid import *\n",
"\n",
"env = gym.make(\"CliffWalking-v1\", render_mode=\"rgb_array\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "869f43bb",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:21.857048Z",
"iopub.status.busy": "2026-03-26T10:48:21.856878Z",
"iopub.status.idle": "2026-03-26T10:48:21.890084Z",
"shell.execute_reply": "2026-03-26T10:48:21.889502Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
" \n",
" Network\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n"
],
"text/plain": [
""
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},
"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": "f8480818",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:21.897103Z",
"iopub.status.busy": "2026-03-26T10:48:21.896877Z",
"iopub.status.idle": "2026-03-26T10:48:22.087202Z",
"shell.execute_reply": "2026-03-26T10:48:22.086629Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"![](\"gifs/cliff.gif\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from stormvogel.stormpy_utils.model_checking import model_checking\n",
"\n",
"res = model_checking(sv_model, '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": "800050ae",
"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": "e7199322",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:22.089092Z",
"iopub.status.busy": "2026-03-26T10:48:22.088916Z",
"iopub.status.idle": "2026-03-26T10:48:22.149079Z",
"shell.execute_reply": "2026-03-26T10:48:22.148632Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'ModelType.MDP model with 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",
"\n",
"env = gym.make(\n",
" \"Taxi-v3\", render_mode=\"rgb_array\"\n",
") # 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": "030143e3",
"metadata": {
"execution": {
"iopub.execute_input": "2026-03-26T10:48:22.150677Z",
"iopub.status.busy": "2026-03-26T10:48:22.150503Z",
"iopub.status.idle": "2026-03-26T10:48:22.772124Z",
"shell.execute_reply": "2026-03-26T10:48:22.771547Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"![](\"gifs/taxi.gif\")
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target = get_target_state(env)\n",
"res = model_checking(sv_model, \"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)"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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