{
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{
"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."
]
},
{
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"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",
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"Output()"
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""
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" 'data': data\n",
" })\n",
" })\n",
" }\n"
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"return_id_result('http://127.0.0.1:8889', 'DrhnqMtJFeNSQmhYXSgY', 'test message')"
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""
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"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Test request failed. See 'Communication server remark' in docs. Disable warning by use_server=False.\n"
]
},
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"Output()"
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},
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"\n",
"\n",
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" \n",
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" \n",
" \n",
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" \n",
" \n",
" \n",
" \n",
"\n"
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"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": "2025-07-11T11:08:13.868760Z",
"iopub.status.busy": "2025-07-11T11:08:13.868386Z",
"iopub.status.idle": "2025-07-11T11:08:13.960146Z",
"shell.execute_reply": "2025-07-11T11:08:13.959604Z"
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},
"metadata": {},
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" \n",
" \n",
"\n"
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"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": "2025-07-11T11:08:14.010340Z",
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"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": "2025-07-11T11:08:27.056836Z",
"iopub.status.busy": "2025-07-11T11:08:27.056672Z",
"iopub.status.idle": "2025-07-11T11:08:27.103216Z",
"shell.execute_reply": "2025-07-11T11:08:27.102692Z"
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"
"
],
"text/plain": [
""
]
},
"metadata": {},
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}
],
"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": "2025-07-11T11:08:27.105131Z",
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"text/html": [
"
"
],
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""
]
},
"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": "2025-07-11T11:08:27.415227Z",
"iopub.status.busy": "2025-07-11T11:08:27.414811Z",
"iopub.status.idle": "2025-07-11T11:08:27.421842Z",
"shell.execute_reply": "2025-07-11T11:08:27.421465Z"
}
},
"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": {
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" \n",
"\n"
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"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": {
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"
"
],
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""
]
},
"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": "2025-07-11T11:08:27.762258Z",
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}
},
"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",
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""
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"source": [
"import IPython.display as ipd\n",
"\n",
"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)"
]
}
],
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