From 993b81917c5aa32fcd594762e6efebfc87e50469 Mon Sep 17 00:00:00 2001
From: Nolwen <nolwen.huet@imacs.polytechnique.fr>
Date: Thu, 7 Nov 2024 15:32:13 +0100
Subject: [PATCH] Add a sb3 algo + policy for domains with graph observations

- we reuse our stable_baselines3 wrapper
- the policy is extracting features from the graph with a GNN
- the GNN is using pytorch-geometric
- We subclass
  - ActorCriticPolicy:
    - feature extractor = gnn
    - custom conversion of observation to torch to convert into
      torch_geometric.data.Data
  - PPO to handle properly
    - observation conversion
    - rollout buffer
- Current limitations:
  - we extract a fixed number of features (independent of edge/node
    numbers) for now as we end with a feature reduction layer connected
    to a classic mlp (not knowning anything about the current graph structure)
- User input: the user can define (and default choices are made else)
  - the gnn (default to a 2 layers GCN), taking as inputs w.r.t torch_geometric conventions:
    - x: nodes features
    - edge_index: edge indices or sparse transposed adjency matrix
    - edge_attr (optional): edges features
    - edge_weight (optional): edge weights (taken from first dimension
      of edge_attr)
  - the feature reduction layer from the gnn output to the fixed number of features
    (default to global_max_pool + linear layer + relu)
---
 .github/workflows/ci.yml                      |  12 +-
 examples/gnn_sb3_jsp.py                       | 140 ++++
 examples/gnn_sb3_maze.py                      | 204 ++++++
 poetry.lock                                   | 617 +++++++++++++++++-
 pyproject.toml                                |   9 +-
 .../solver/stable_baselines/gnn/__init__.py   |   1 +
 .../stable_baselines/gnn/common/__init__.py   |   0
 .../stable_baselines/gnn/common/buffers.py    | 132 ++++
 .../gnn/common/on_policy_algorithm.py         | 171 +++++
 .../stable_baselines/gnn/common/policies.py   | 112 ++++
 .../gnn/common/torch_layers.py                | 115 ++++
 .../stable_baselines/gnn/common/utils.py      |  34 +
 .../gnn/common/vec_env/__init__.py            |   0
 .../gnn/common/vec_env/dummy_vec_env.py       |  71 ++
 .../stable_baselines/gnn/ppo/__init__.py      |   4 +
 .../solver/stable_baselines/gnn/ppo/ppo.py    |  13 +
 .../stable_baselines/stable_baselines.py      |   8 +-
 tests/solvers/python/test_gnn_sb3.py          | 417 ++++++++++++
 18 files changed, 2032 insertions(+), 28 deletions(-)
 create mode 100644 examples/gnn_sb3_jsp.py
 create mode 100644 examples/gnn_sb3_maze.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/__init__.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/__init__.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/buffers.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/on_policy_algorithm.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/policies.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/torch_layers.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/utils.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/vec_env/__init__.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/common/vec_env/dummy_vec_env.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/ppo/__init__.py
 create mode 100644 skdecide/hub/solver/stable_baselines/gnn/ppo/ppo.py
 create mode 100644 tests/solvers/python/test_gnn_sb3.py

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b2f0c5adf8..997426854e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -564,9 +564,9 @@ jobs:
           python_version=${{ matrix.python-version }}
           wheelfile=$(ls ./wheels/scikit_decide*-cp${python_version/\./}-*win*.whl)
           if [ "$python_version" = "3.12" ]; then
-            pip install ${wheelfile}[all] pytest "pygame>=2.5" optuna "cffi>=1.17"
+            pip install ${wheelfile}[all] pytest "pygame>=2.5" optuna "cffi>=1.17" graph-jsp-env pytest-cases
           else
-            pip install ${wheelfile}[all] pytest gymnasium[classic-control] optuna
+            pip install ${wheelfile}[all] pytest gymnasium[classic-control] optuna graph-jsp-env pytest-cases
           fi
 
       - name: Test with pytest
@@ -662,9 +662,9 @@ jobs:
           arch=$(uname -m)
           wheelfile=$(ls ./wheels/scikit_decide*-cp${python_version/\./}-*macos*${arch}.whl)
           if [ "$python_version" = "3.12" ]; then
-            pip install ${wheelfile}[all] pytest "pygame>=2.5" optuna "cffi>=1.17"
+            pip install ${wheelfile}[all] pytest "pygame>=2.5" optuna "cffi>=1.17" graph-jsp-env pytest-cases
           else
-            pip install ${wheelfile}[all] pytest gymnasium[classic-control] optuna
+            pip install ${wheelfile}[all] pytest gymnasium[classic-control] optuna graph-jsp-env pytest-cases
           fi
 
       - name: Test with pytest
@@ -762,9 +762,9 @@ jobs:
           python_version=${{ matrix.python-version }}
           wheelfile=$(ls ./wheels/scikit_decide*-cp${python_version/\./}-*manylinux*.whl)
           if [ "$python_version" = "3.12" ]; then
-            pip install ${wheelfile}[all] pytest "pygame>=2.5" "cffi>=1.17" docopt commonmark optuna
+            pip install ${wheelfile}[all] pytest "pygame>=2.5" "cffi>=1.17" docopt commonmark optuna graph-jsp-env pytest-cases
           else
-            pip install ${wheelfile}[all] pytest gymnasium[classic-control] docopt commonmark optuna
+            pip install ${wheelfile}[all] pytest gymnasium[classic-control] docopt commonmark optuna graph-jsp-env pytest-cases
           fi
 
       - name: Test with pytest
diff --git a/examples/gnn_sb3_jsp.py b/examples/gnn_sb3_jsp.py
new file mode 100644
index 0000000000..268a13a812
--- /dev/null
+++ b/examples/gnn_sb3_jsp.py
@@ -0,0 +1,140 @@
+from typing import Any
+
+import numpy as np
+from graph_jsp_env.disjunctive_graph_jsp_env import DisjunctiveGraphJspEnv
+from gymnasium.spaces import Box, Graph, GraphInstance
+
+from skdecide.core import Space, TransitionOutcome, Value
+from skdecide.domains import Domain
+from skdecide.hub.domain.gym import GymDomain
+from skdecide.hub.solver.stable_baselines import StableBaseline
+from skdecide.hub.solver.stable_baselines.gnn import GraphPPO
+from skdecide.hub.space.gym import GymSpace, ListSpace
+from skdecide.utils import rollout
+
+# JSP graph env
+
+
+class D(Domain):
+    T_state = GraphInstance  # Type of states
+    T_observation = T_state  # Type of observations
+    T_event = int  # Type of events
+    T_value = float  # Type of transition values (rewards or costs)
+    T_info = None  # Type of additional information in environment outcome
+
+
+class GraphJspDomain(GymDomain, D):
+    _gym_env: DisjunctiveGraphJspEnv
+
+    def __init__(self, gym_env):
+        GymDomain.__init__(self, gym_env=gym_env)
+        if self._gym_env.normalize_observation_space:
+            self.n_nodes_features = gym_env.n_machines + 1
+        else:
+            self.n_nodes_features = 2
+
+    def _state_step(
+        self, action: D.T_event
+    ) -> TransitionOutcome[D.T_state, Value[D.T_value], D.T_predicate, D.T_info]:
+        outcome = super()._state_step(action=action)
+        outcome.state = self._np_state2graph_state(outcome.state)
+        return outcome
+
+    def _get_applicable_actions_from(
+        self, memory: D.T_memory[D.T_state]
+    ) -> D.T_agent[Space[D.T_event]]:
+        return ListSpace(np.nonzero(self._gym_env.valid_action_mask())[0])
+
+    def _is_applicable_action_from(
+        self, action: D.T_agent[D.T_event], memory: D.T_memory[D.T_state]
+    ) -> bool:
+        return self._gym_env.valid_action_mask()[action]
+
+    def _state_reset(self) -> D.T_state:
+        return self._np_state2graph_state(super()._state_reset())
+
+    def _get_observation_space_(self) -> Space[D.T_observation]:
+        if self._gym_env.normalize_observation_space:
+            original_graph_space = Graph(
+                node_space=Box(
+                    low=0.0, high=1.0, shape=(self.n_nodes_features,), dtype=np.float_
+                ),
+                edge_space=Box(low=0, high=1.0, dtype=np.float_),
+            )
+
+        else:
+            original_graph_space = Graph(
+                node_space=Box(
+                    low=np.array([0, 0]),
+                    high=np.array(
+                        [
+                            self._gym_env.n_machines,
+                            self._gym_env.longest_processing_time,
+                        ]
+                    ),
+                    dtype=np.int_,
+                ),
+                edge_space=Box(
+                    low=0, high=self._gym_env.longest_processing_time, dtype=np.int_
+                ),
+            )
+        return GymSpace(original_graph_space)
+
+    def _np_state2graph_state(self, np_state: np.array) -> GraphInstance:
+        if not self._gym_env.normalize_observation_space:
+            np_state = np_state.astype(np.int_)
+
+        nodes = np_state[:, -self.n_nodes_features :]
+        adj = np_state[:, : -self.n_nodes_features]
+        edge_starts_ends = adj.nonzero()
+        edge_links = np.transpose(edge_starts_ends)
+        edges = adj[edge_starts_ends][:, None]
+
+        return GraphInstance(nodes=nodes, edges=edges, edge_links=edge_links)
+
+    def _render_from(self, memory: D.T_memory[D.T_state], **kwargs: Any) -> Any:
+        return self._gym_env.render(**kwargs)
+
+
+jsp = np.array(
+    [
+        [
+            [0, 1, 2],  # machines for job 0
+            [0, 2, 1],  # machines for job 1
+            [0, 1, 2],  # machines for job 2
+        ],
+        [
+            [3, 2, 2],  # task durations of job 0
+            [2, 1, 4],  # task durations of job 1
+            [0, 4, 3],  # task durations of job 2
+        ],
+    ]
+)
+
+
+jsp_env = DisjunctiveGraphJspEnv(
+    jps_instance=jsp,
+    perform_left_shift_if_possible=True,
+    normalize_observation_space=False,
+    flat_observation_space=False,
+    action_mode="task",
+)
+
+
+# random rollout
+domain = GraphJspDomain(gym_env=jsp_env)
+rollout(domain=domain, max_steps=jsp_env.total_tasks_without_dummies, num_episodes=1)
+
+# solve with sb3-PPO-GNN
+domain_factory = lambda: GraphJspDomain(gym_env=jsp_env)
+with StableBaseline(
+    domain_factory=domain_factory,
+    algo_class=GraphPPO,
+    baselines_policy="GraphInputPolicy",
+    learn_config={"total_timesteps": 100},
+    # batch_size=1,
+    # normalize_advantage=False
+) as solver:
+
+    solver.solve()
+    rollout(domain=domain_factory(), solver=solver, max_steps=100, num_episodes=1)
diff --git a/examples/gnn_sb3_maze.py b/examples/gnn_sb3_maze.py
new file mode 100644
index 0000000000..923b3b8a5d
--- /dev/null
+++ b/examples/gnn_sb3_maze.py
@@ -0,0 +1,204 @@
+from typing import Any, Optional
+
+import numpy as np
+from gymnasium.spaces import Box, Discrete, Graph, GraphInstance
+
+from skdecide.builders.domain import Renderable, UnrestrictedActions
+from skdecide.core import Space, Value
+from skdecide.domains import DeterministicPlanningDomain
+from skdecide.hub.domain.maze import Maze
+from skdecide.hub.domain.maze.maze import DEFAULT_MAZE, Action, State
+from skdecide.hub.solver.stable_baselines import StableBaseline
+from skdecide.hub.solver.stable_baselines.gnn import GraphPPO
+from skdecide.hub.space.gym import GymSpace, ListSpace
+from skdecide.utils import rollout
+
+
+class D(DeterministicPlanningDomain, UnrestrictedActions, Renderable):
+    T_state = GraphInstance  # Type of states
+    T_observation = T_state  # Type of observations
+    T_event = Action  # Type of events
+    T_value = float  # Type of transition values (rewards or costs)
+    T_predicate = bool  # Type of logical checks
+    T_info = (
+        None  # Type of additional information given as part of an environment outcome
+    )
+
+
+class GraphMaze(D):
+    def __init__(self, maze_str: str = DEFAULT_MAZE, discrete_features: bool = False):
+        self.discrete_features = discrete_features
+        self.maze_domain = Maze(maze_str=maze_str)
+        np_wall = np.array(self.maze_domain._maze)
+        np_y = np.array(
+            [
+                [(i) for j in range(self.maze_domain._num_cols)]
+                for i in range(self.maze_domain._num_rows)
+            ]
+        )
+        np_x = np.array(
+            [
+                [(j) for j in range(self.maze_domain._num_cols)]
+                for i in range(self.maze_domain._num_rows)
+            ]
+        )
+        walls = np_wall.ravel()
+        coords = [i for i in zip(np_y.ravel(), np_x.ravel())]
+        np_node_id = np.reshape(range(len(walls)), np_wall.shape)
+        edge_links = []
+        edges = []
+        for i in range(self.maze_domain._num_rows):
+            for j in range(self.maze_domain._num_cols):
+                current_coord = (i, j)
+                if i > 0:
+                    next_coord = (i - 1, j)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if i < self.maze_domain._num_rows - 1:
+                    next_coord = (i + 1, j)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if j > 0:
+                    next_coord = (i, j - 1)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if j < self.maze_domain._num_cols - 1:
+                    next_coord = (i, j + 1)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+        self.edges = np.array(edges)
+        self.edge_links = np.array(edge_links)
+        self.walls = walls
+        self.node_ids = np_node_id
+        self.coords = coords
+
+    def _mazestate2graph(self, state: State) -> GraphInstance:
+        x, y = state
+        agent_presence = np.zeros(self.walls.shape, dtype=self.walls.dtype)
+        agent_presence[self.node_ids[y, x]] = 1
+        nodes = np.stack([self.walls, agent_presence], axis=-1)
+        if self.discrete_features:
+            return GraphInstance(
+                nodes=nodes, edges=self.edges, edge_links=self.edge_links
+            )
+        else:
+            return GraphInstance(
+                nodes=nodes, edges=self.edges[:, None], edge_links=self.edge_links
+            )
+
+    def _graph2mazestate(self, graph: GraphInstance) -> State:
+        y, x = self.coords[graph.nodes[:, 1].nonzero()[0][0]]
+        return State(x=x, y=y)
+
+    def _is_terminal(self, state: D.T_state) -> D.T_predicate:
+        return self.maze_domain._is_terminal(self._graph2mazestate(state))
+
+    def _get_next_state(self, memory: D.T_state, action: D.T_event) -> D.T_state:
+        maze_memory = self._graph2mazestate(memory)
+        maze_next_state = self.maze_domain._get_next_state(
+            memory=maze_memory, action=action
+        )
+        return self._mazestate2graph(maze_next_state)
+
+    def _get_transition_value(
+        self,
+        memory: D.T_state,
+        action: D.T_event,
+        next_state: Optional[D.T_state] = None,
+    ) -> Value[D.T_value]:
+        maze_memory = self._graph2mazestate(memory)
+        if next_state is None:
+            maze_next_state = None
+        else:
+            maze_next_state = self._graph2mazestate(next_state)
+        return self.maze_domain._get_transition_value(
+            memory=maze_memory, action=action, next_state=maze_next_state
+        )
+
+    def _get_action_space_(self) -> Space[D.T_event]:
+        return self.maze_domain._get_action_space_()
+
+    def _get_goals_(self) -> Space[D.T_observation]:
+        return ListSpace([self._mazestate2graph(self.maze_domain._goal)])
+
+    def _is_goal(
+        self, observation: D.T_agent[D.T_observation]
+    ) -> D.T_agent[D.T_predicate]:
+        return self.maze_domain._is_goal(self._graph2mazestate(observation))
+
+    def _get_initial_state_(self) -> D.T_state:
+        return self._mazestate2graph(self.maze_domain._get_initial_state_())
+
+    def _get_observation_space_(self) -> Space[D.T_observation]:
+        if self.discrete_features:
+            return GymSpace(
+                Graph(
+                    node_space=Box(low=0, high=1, shape=(2,), dtype=self.walls.dtype),
+                    edge_space=Discrete(2),
+                )
+            )
+        else:
+            return GymSpace(
+                Graph(
+                    node_space=Box(low=0, high=1, shape=(2,), dtype=self.walls.dtype),
+                    edge_space=Box(low=0, high=1, shape=(1,), dtype=self.edges.dtype),
+                )
+            )
+
+    def _render_from(self, memory: D.T_state, **kwargs: Any) -> Any:
+        maze_memory = self._graph2mazestate(memory)
+        self.maze_domain._render_from(memory=maze_memory, **kwargs)
+
+
+MAZE = """
++-+-+-+-+o+-+-+--+-+-+
+|   |              | |
++ + + +-+-+-+ +--+ + +
+| | |   |   | |  |   |
++ +-+-+ +-+ + + -+ +-+
+| |   |   | |    |   |
++ + + + + + + +--+ +-+
+|   |   |   | |      |
++-+-+-+-+-+-+-+ -+-+ +
+|             |    | |
++ +-+-+-+-+ + +--+-+ +
+|   |       |        |
++ + + +-+ +-+ +--+-+-+
+| | |   |     |      |
++ +-+-+ + +-+ + -+-+ +
+| |     | | | |    | |
++-+ +-+ + + + +--+ + +
+|   |   |   |    | | |
++ +-+ +-+-+-+-+ -+ + +
+|   |       |      | |
++-+-+-+-+-+x+-+--+-+-+
+"""
+
+domain = GraphMaze(maze_str=MAZE, discrete_features=True)
+assert domain.reset() in domain.get_observation_space()
+
+# random rollout
+rollout(domain=domain, max_steps=50, num_episodes=1)
+
+# solve with sb3-PPO-GNN
+domain_factory = lambda: GraphMaze(maze_str=MAZE)
+max_steps = domain.maze_domain._num_cols * domain.maze_domain._num_rows
+with StableBaseline(
+    domain_factory=domain_factory,
+    algo_class=GraphPPO,
+    baselines_policy="GraphInputPolicy",
+    learn_config={"total_timesteps": 100},
+    # batch_size=1,
+    # normalize_advantage=False
+) as solver:
+
+    solver.solve()
+    rollout(domain=domain_factory(), solver=solver, max_steps=max_steps, num_episodes=1)
diff --git a/poetry.lock b/poetry.lock
index 4a4644b702..1cd3b25a20 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -11,6 +11,115 @@ files = [
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
 ]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+description = "Happy Eyeballs for asyncio"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.6"
+description = "Async http client/server framework (asyncio)"
+optional = true
+python-versions = ">=3.9"
+files = [
+    {file = "aiohttp-3.11.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7510b3ca2275691875ddf072a5b6cd129278d11fe09301add7d292fc8d3432de"},
+    {file = "aiohttp-3.11.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bfab0d2c3380c588fc925168533edb21d3448ad76c3eadc360ff963019161724"},
+    {file = "aiohttp-3.11.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cf02dba0f342f3a8228f43fae256aafc21c4bc85bffcf537ce4582e2b1565188"},
+    {file = "aiohttp-3.11.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:92daedf7221392e7a7984915ca1b0481a94c71457c2f82548414a41d65555e70"},
+    {file = "aiohttp-3.11.6-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2274a7876e03429e3218589a6d3611a194bdce08c3f1e19962e23370b47c0313"},
+    {file = "aiohttp-3.11.6-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8a2e1eae2d2f62f3660a1591e16e543b2498358593a73b193006fb89ee37abc6"},
+    {file = "aiohttp-3.11.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:978ec3fb0a42efcd98aae608f58c6cfcececaf0a50b4e86ee3ea0d0a574ab73b"},
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+[package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
+aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
+attrs = ">=17.3.0"
+frozenlist = ">=1.1.1"
+multidict = ">=4.5,<7.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
+
+[package.extras]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
+
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
@@ -69,6 +178,17 @@ files = [
 six = ">=1.6.1,<2.0"
 wheel = ">=0.23.0,<1.0"
 
+[[package]]
+name = "async-timeout"
+version = "5.0.1"
+description = "Timeout context manager for asyncio programs"
+optional = true
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "atomicwrites"
 version = "1.4.1"
@@ -496,7 +616,7 @@ cffi = "*"
 name = "cloudpickle"
 version = "3.1.0"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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@@ -827,6 +947,20 @@ files = [
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 ]
 
+[[package]]
+name = "decopatch"
+version = "1.4.10"
+description = "Create decorators easily in python."
+optional = false
+python-versions = "*"
+files = [
+    {file = "decopatch-1.4.10-py2.py3-none-any.whl", hash = "sha256:e151f7f93de2b1b3fd3f3272dcc7cefd1a69f68ec1c2d8e288ecd9deb36dc5f7"},
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+]
+
+[package.dependencies]
+makefun = ">=1.5.0"
+
 [[package]]
 name = "decorator"
 version = "5.1.1"
@@ -1080,7 +1214,7 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 name = "farama-notifications"
 version = "0.0.4"
 description = "Notifications for all Farama Foundation maintained libraries."
-optional = true
+optional = false
 python-versions = "*"
 files = [
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@@ -1365,6 +1499,32 @@ files = [
 [package.dependencies]
 six = "*"
 
+[[package]]
+name = "graph-jsp-env"
+version = "0.3.3"
+description = "A flexible enviorment for job shop scheduling using the disjunctive graph apporach."
+optional = false
+python-versions = ">=3.9"
+files = [
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+]
+
+[package.dependencies]
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+kaleido = "*"
+matplotlib = "*"
+networkx = ">=3"
+numpy = "*"
+opencv-python = "*"
+pandas = "*"
+plotly = "*"
+rich = "*"
+
+[package.extras]
+dev = ["black", "bumpver", "flake8", "isort", "mypy", "pip-tools", "pytest", "pytest-cov", "stable-baselines3"]
+testing = ["flake8 (>=3.9)", "mypy (>=0.910)", "pytest (>=6.0)", "pytest-cov (>=2.0)", "tox (>=3.24)"]
+
 [[package]]
 name = "grpcio"
 version = "1.67.0"
@@ -1468,7 +1628,7 @@ matrixapi = ["numpy", "scipy"]
 name = "gymnasium"
 version = "0.28.1"
 description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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@@ -1735,8 +1895,8 @@ importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
 jaxlib = ">=0.4.27,<=0.4.30"
 ml-dtypes = ">=0.2.0"
 numpy = [
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.22", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 opt-einsum = "*"
@@ -1758,7 +1918,7 @@ tpu = ["jaxlib (==0.4.30)", "libtpu-nightly (==0.1.dev20240617)", "requests"]
 name = "jax-jumpy"
 version = "1.0.0"
 description = "Common backend for Jax or Numpy."
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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@@ -1951,6 +2111,16 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "kaleido"
+version = "0.2.1.post1"
+description = "Static image export for web-based visualization libraries with zero dependencies"
+optional = false
+python-versions = "*"
+files = [
+    {file = "kaleido-0.2.1.post1-py2.py3-none-manylinux2014_armv7l.whl", hash = "sha256:d313940896c24447fc12c74f60d46ea826195fc991f58569a6e73864d53e5c20"},
+]
+
 [[package]]
 name = "keras"
 version = "3.6.0"
@@ -2213,6 +2383,17 @@ docs = ["sphinx (>=1.6.0)", "sphinx-bootstrap-theme"]
 flake8 = ["flake8"]
 tests = ["psutil", "pytest (!=3.3.0)", "pytest-cov"]
 
+[[package]]
+name = "makefun"
+version = "1.15.6"
+description = "Small library to dynamically create python functions."
+optional = false
+python-versions = "*"
+files = [
+    {file = "makefun-1.15.6-py2.py3-none-any.whl", hash = "sha256:e69b870f0bb60304765b1e3db576aaecf2f9b3e5105afe8cfeff8f2afe6ad067"},
+    {file = "makefun-1.15.6.tar.gz", hash = "sha256:26bc63442a6182fb75efed8b51741dd2d1db2f176bec8c64e20a586256b8f149"},
+]
+
 [[package]]
 name = "markdown"
 version = "3.7"
@@ -2235,7 +2416,7 @@ testing = ["coverage", "pyyaml"]
 name = "markdown-it-py"
 version = "3.0.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
@@ -2407,7 +2588,7 @@ traitlets = "*"
 name = "mdurl"
 version = "0.1.2"
 description = "Markdown URL utilities"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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@@ -2456,9 +2637,9 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
-    {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">1.20", markers = "python_version < \"3.10\""},
+    {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
@@ -2566,6 +2747,110 @@ files = [
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 ]
 
+[[package]]
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+version = "6.1.0"
+description = "multidict implementation"
+optional = true
+python-versions = ">=3.8"
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+
 [[package]]
 name = "multiprocess"
 version = "0.70.17"
@@ -2945,6 +3230,32 @@ pandas = ">=1.2"
 pyyaml = ">=5.1"
 scipy = ">=1.7"
 
+[[package]]
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+version = "4.10.0.84"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
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+    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
+    {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
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+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+]
+
 [[package]]
 name = "opt-einsum"
 version = "3.4.0"
@@ -3195,8 +3506,8 @@ files = [
 
 [package.dependencies]
 numpy = [
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     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
@@ -3382,6 +3693,21 @@ docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-a
 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
 type = ["mypy (>=1.11.2)"]
 
+[[package]]
+name = "plotly"
+version = "5.24.1"
+description = "An open-source, interactive data visualization library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+tenacity = ">=6.2.0"
+
 [[package]]
 name = "pluggy"
 version = "1.5.0"
@@ -3447,6 +3773,113 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
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+
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 version = "5.28.3"
@@ -4071,6 +4504,22 @@ toml = "*"
 [package.extras]
 testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
 
+[[package]]
+name = "pytest-cases"
+version = "3.8.6"
+description = "Separate test code from test cases in pytest."
+optional = false
+python-versions = "*"
+files = [
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+
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+decopatch = "*"
+makefun = ">=1.15.1"
+packaging = "*"
+
 [[package]]
 name = "pytest-cov"
 version = "2.12.1"
@@ -4447,7 +4896,7 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 name = "rich"
 version = "13.9.3"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = true
+optional = false
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 [package.extras]
 widechars = ["wcwidth"]
 
+[[package]]
+name = "tenacity"
+version = "9.0.0"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
 name = "tensorboard"
 version = "2.18.0"
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 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.12.0)"]
 
+[[package]]
+name = "torch-geometric"
+version = "2.6.1"
+description = "Graph Neural Network Library for PyTorch"
+optional = true
+python-versions = ">=3.8"
+files = [
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+numpy = "*"
+psutil = ">=5.8.0"
+pyparsing = "*"
+requests = "*"
+tqdm = "*"
+
+[package.extras]
+benchmark = ["matplotlib", "networkx", "pandas", "protobuf (<4.21)", "wandb"]
+dev = ["ipython", "matplotlib-inline", "pre-commit", "torch_geometric[test]"]
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+graphgym = ["protobuf (<4.21)", "pytorch-lightning (<2.3.0)", "yacs"]
+modelhub = ["huggingface_hub"]
+test = ["onnx", "onnxruntime", "pytest", "pytest-cov"]
+
 [[package]]
 name = "tornado"
 version = "6.4.1"
@@ -5550,6 +6043,102 @@ files = [
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+    {file = "yarl-1.17.2-cp39-cp39-win_amd64.whl", hash = "sha256:48e424347a45568413deec6f6ee2d720de2cc0385019bedf44cd93e8638aa0ed"},
+    {file = "yarl-1.17.2-py3-none-any.whl", hash = "sha256:dd7abf4f717e33b7487121faf23560b3a50924f80e4bef62b22dab441ded8f3b"},
+    {file = "yarl-1.17.2.tar.gz", hash = "sha256:753eaaa0c7195244c84b5cc159dc8204b7fd99f716f11198f999f2332a86b178"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
 [[package]]
 name = "zipp"
 version = "3.20.2"
@@ -5570,11 +6159,11 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 type = ["pytest-mypy"]
 
 [extras]
-all = ["cartopy", "gymnasium", "joblib", "matplotlib", "numpy", "openap", "pyRDDLGym", "pyRDDLGym", "pyRDDLGym-gurobi", "pyRDDLGym-jax", "pyRDDLGym-rl", "pyRDDLGym-rl", "pygeodesy", "pygrib", "pygrib", "ray", "rddlrepository", "scipy", "stable-baselines3", "unified-planning", "up-enhsp", "up-fast-downward", "up-pyperplan", "up-tamer"]
+all = ["cartopy", "gymnasium", "joblib", "matplotlib", "numpy", "openap", "pyRDDLGym", "pyRDDLGym", "pyRDDLGym-gurobi", "pyRDDLGym-jax", "pyRDDLGym-rl", "pyRDDLGym-rl", "pygeodesy", "pygrib", "pygrib", "ray", "rddlrepository", "scipy", "stable-baselines3", "torch-geometric", "unified-planning", "up-enhsp", "up-fast-downward", "up-pyperplan", "up-tamer"]
 domains = ["cartopy", "gymnasium", "matplotlib", "numpy", "openap", "pyRDDLGym", "pyRDDLGym", "pyRDDLGym-rl", "pyRDDLGym-rl", "pygeodesy", "pygrib", "pygrib", "rddlrepository", "scipy", "unified-planning"]
-solvers = ["gymnasium", "joblib", "numpy", "pyRDDLGym-gurobi", "pyRDDLGym-jax", "ray", "scipy", "stable-baselines3", "unified-planning", "up-enhsp", "up-fast-downward", "up-pyperplan", "up-tamer"]
+solvers = ["gymnasium", "joblib", "numpy", "pyRDDLGym-gurobi", "pyRDDLGym-jax", "ray", "scipy", "stable-baselines3", "torch-geometric", "unified-planning", "up-enhsp", "up-fast-downward", "up-pyperplan", "up-tamer"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "353bf8a69a6c318e17e71f400e98ea6d4392883291de54e4c4570cea0a9288f2"
+content-hash = "2b17ad02ae15987e4983858da614b1d71e27e1ef9ff9ed1e4b95d32e1ed02813"
diff --git a/pyproject.toml b/pyproject.toml
index 8fcad3a225..44b62ba7b6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,6 +82,7 @@ pyRDDLGym-rl = [
 pyRDDLGym-jax = { version = ">=0.3", optional = true }
 pyRDDLGym-gurobi = { version = ">=0.2", optional = true }
 rddlrepository = {version = ">=2.0", optional = true }
+torch-geometric = {version = ">=2.5", optional = true}
 
 [tool.poetry.extras]
 domains = [
@@ -111,7 +112,8 @@ solvers = [
     "up-pyperplan",
     "scipy",
     "pyRDDLGym-jax",
-    "pyRDDLGym-gurobi"
+    "pyRDDLGym-gurobi",
+    "torch-geometric"
 ]
 all = [
     "gymnasium",
@@ -134,7 +136,8 @@ all = [
     "pyRDDLGym-rl",
     "rddlrepository",
     "pyRDDLGym-jax",
-    "pyRDDLGym-gurobi"
+    "pyRDDLGym-gurobi",
+    "torch-geometric"
 ]
 
 [tool.poetry.plugins."skdecide.domains"]
@@ -199,6 +202,8 @@ commonmark = ">=0.9.1"
 gymnasium = { version = ">=0.28.1", extras = [
     "classic-control",
 ], optional = true }
+graph-jsp-env =  { version = ">=0.3.3"}
+pytest-cases = {version = ">=3.8"}
 
 [tool.pytest.ini_options]
 minversion = "6.0"
diff --git a/skdecide/hub/solver/stable_baselines/gnn/__init__.py b/skdecide/hub/solver/stable_baselines/gnn/__init__.py
new file mode 100644
index 0000000000..261a27e78b
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/__init__.py
@@ -0,0 +1 @@
+from .ppo import GraphPPO
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/__init__.py b/skdecide/hub/solver/stable_baselines/gnn/common/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/buffers.py b/skdecide/hub/solver/stable_baselines/gnn/common/buffers.py
new file mode 100644
index 0000000000..602e784ba8
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/buffers.py
@@ -0,0 +1,132 @@
+from collections.abc import Generator
+from typing import Optional, Union
+
+import numpy as np
+import torch as th
+import torch_geometric as thg
+from gymnasium import spaces
+from stable_baselines3.common.buffers import RolloutBuffer
+from stable_baselines3.common.preprocessing import get_action_dim
+from stable_baselines3.common.type_aliases import (
+    DictRolloutBufferSamples,
+    RolloutBufferSamples,
+)
+from stable_baselines3.common.utils import get_device
+from stable_baselines3.common.vec_env import VecNormalize
+
+from .utils import copy_graph_instance, graph_obs_to_thg_data
+
+
+class GraphRolloutBuffer(RolloutBuffer):
+    observations: Union[list[spaces.GraphInstance], list[list[spaces.GraphInstance]]]
+
+    def __init__(
+        self,
+        buffer_size: int,
+        observation_space: spaces.Graph,
+        action_space: spaces.Space,
+        device: Union[th.device, str] = "auto",
+        gae_lambda: float = 1,
+        gamma: float = 0.99,
+        n_envs: int = 1,
+    ):
+        self.buffer_size = buffer_size
+        self.observation_space = observation_space
+        self.action_space = action_space
+        self.obs_shape = (
+            observation_space.node_space.shape + observation_space.edge_space.shape
+        )
+        self.action_dim = get_action_dim(action_space)
+        self.pos = 0
+        self.full = False
+        self.device = get_device(device)
+        self.n_envs = n_envs
+        self.gae_lambda = gae_lambda
+        self.gamma = gamma
+        self.generator_ready = False
+        self.reset()
+
+    def reset(self) -> None:
+        assert isinstance(
+            self.observation_space, spaces.Graph
+        ), "GraphRolloutBuffer must be used with Graph obs space only"
+        super().reset()
+        self.observations = list()  # try to use list to save torch_geometric data
+
+    def add(
+        self,
+        obs: spaces.GraphInstance,
+        action: np.ndarray,
+        reward: np.ndarray,
+        episode_start: np.ndarray,
+        value: th.Tensor,
+        log_prob: th.Tensor,
+    ) -> None:
+        if len(log_prob.shape) == 0:
+            # Reshape 0-d tensor to avoid error
+            log_prob = log_prob.reshape(-1, 1)
+
+        # Reshape needed when using multiple envs with discrete observations
+        # as numpy cannot broadcast (n_discrete,) to (n_discrete, 1)
+        if isinstance(self.observation_space, spaces.Discrete):
+            obs = obs.reshape((self.n_envs,) + self.obs_shape)
+
+        # Same reshape, for actions
+        action = action.reshape((self.n_envs, self.action_dim))
+
+        self.observations.append([copy_graph_instance(g) for g in obs])
+        self.actions[self.pos] = np.array(action).copy()
+        self.rewards[self.pos] = np.array(reward).copy()
+        self.episode_starts[self.pos] = np.array(episode_start).copy()
+        self.values[self.pos] = value.clone().cpu().numpy().flatten()
+        self.log_probs[self.pos] = log_prob.clone().cpu().numpy()
+        self.pos += 1
+        if self.pos == self.buffer_size:
+            self.full = True
+
+    def get(
+        self, batch_size: Optional[int] = None
+    ) -> Generator[DictRolloutBufferSamples, None, None]:
+        assert self.full, ""
+        indices = np.random.permutation(self.buffer_size * self.n_envs)
+        # Prepare the data
+        if not self.generator_ready:
+            # can only be used when venv = 1
+            self.raw_observations: list[spaces.GraphInstance] = list()
+            for vec_obs in self.observations:
+                self.raw_observations.extend(vec_obs)
+            self.observations = self.raw_observations
+            _tensor_names = ["actions", "values", "log_probs", "advantages", "returns"]
+
+            for tensor in _tensor_names:
+                self.__dict__[tensor] = self.swap_and_flatten(self.__dict__[tensor])
+            self.generator_ready = True
+
+        # Return everything, don't create minibatches
+        if batch_size is None:
+            batch_size = self.buffer_size * self.n_envs
+
+        start_idx = 0
+        while start_idx < self.buffer_size * self.n_envs:
+            yield self._get_samples(indices[start_idx : start_idx + batch_size])
+            start_idx += batch_size
+
+    def _get_samples(
+        self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None
+    ) -> RolloutBufferSamples:
+        selected_observations = thg.data.Batch.from_data_list(
+            [
+                graph_obs_to_thg_data(self.observations[idx], device=self.device)
+                for idx in batch_inds
+            ]
+        )
+        data = (
+            self.actions[batch_inds],
+            self.values[batch_inds].flatten(),
+            self.log_probs[batch_inds].flatten(),
+            self.advantages[batch_inds].flatten(),
+            self.returns[batch_inds].flatten(),
+        )
+        return RolloutBufferSamples(
+            selected_observations, *tuple(map(self.to_torch, data))
+        )
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/on_policy_algorithm.py b/skdecide/hub/solver/stable_baselines/gnn/common/on_policy_algorithm.py
new file mode 100644
index 0000000000..6824c75e21
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/on_policy_algorithm.py
@@ -0,0 +1,171 @@
+from typing import Optional, Union
+
+import numpy as np
+import torch as th
+from gymnasium import spaces
+from stable_baselines3.common.buffers import RolloutBuffer
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
+from stable_baselines3.common.policies import ActorCriticPolicy
+from stable_baselines3.common.type_aliases import GymEnv
+from stable_baselines3.common.utils import obs_as_tensor
+from stable_baselines3.common.vec_env import VecEnv
+
+from .buffers import GraphRolloutBuffer
+from .utils import graph_obs_to_thg_data
+from .vec_env.dummy_vec_env import wrap_graph_env
+
+
+class GraphOnPolicyAlgorithm(OnPolicyAlgorithm):
+    """Base class for On-Policy algorithms (ex: A2C/PPO) with graph observations."""
+
+    def __init__(
+        self,
+        policy: Union[str, type[ActorCriticPolicy]],
+        env: GymEnv,
+        rollout_buffer_class: Optional[type[RolloutBuffer]] = None,
+        **kwargs,
+    ):
+
+        # Use proper default rollout buffer class
+        if rollout_buffer_class is None:
+            rollout_buffer_class = GraphRolloutBuffer
+        # Use proper VecEnv wrapper for env with Graph spaces
+        env = wrap_graph_env(env)
+        if env.num_envs > 1:
+            raise NotImplementedError(
+                "GraphOnPolicyAlgorithm not implemented for real vectorized environment "
+                "(ie. with more than 1 wrapped environment)"
+            )
+
+        super().__init__(
+            policy=policy,
+            env=env,
+            rollout_buffer_class=rollout_buffer_class,
+            **kwargs,
+        )
+
+    def collect_rollouts(
+        self,
+        env: VecEnv,
+        callback: BaseCallback,
+        rollout_buffer: RolloutBuffer,
+        n_rollout_steps: int,
+    ) -> bool:
+        """
+        Collect experiences using the current policy and fill a ``RolloutBuffer``.
+        The term rollout here refers to the model-free notion and should not
+        be used with the concept of rollout used in model-based RL or planning.
+
+        :param env: The training environment
+        :param callback: Callback that will be called at each step
+            (and at the beginning and end of the rollout)
+        :param rollout_buffer: Buffer to fill with rollouts
+        :param n_rollout_steps: Number of experiences to collect per environment
+        :return: True if function returned with at least `n_rollout_steps`
+            collected, False if callback terminated rollout prematurely.
+        """
+        assert self._last_obs is not None, "No previous observation was provided"
+        # Switch to eval mode (this affects batch norm / dropout)
+        self.policy.set_training_mode(False)
+
+        n_steps = 0
+        rollout_buffer.reset()
+        # Sample new weights for the state dependent exploration
+        if self.use_sde:
+            self.policy.reset_noise(env.num_envs)
+
+        callback.on_rollout_start()
+
+        while n_steps < n_rollout_steps:
+            if (
+                self.use_sde
+                and self.sde_sample_freq > 0
+                and n_steps % self.sde_sample_freq == 0
+            ):
+                # Sample a new noise matrix
+                self.policy.reset_noise(env.num_envs)
+
+            with th.no_grad():
+                # Convert to pytorch tensor or to TensorDict
+                if isinstance(self._last_obs[0], spaces.GraphInstance):
+                    obs_tensor = graph_obs_to_thg_data(
+                        self._last_obs[0], device=self.device
+                    )
+                else:
+                    obs_tensor = obs_as_tensor(self._last_obs, self.device)
+                actions, values, log_probs = self.policy(obs_tensor)
+            actions = actions.cpu().numpy()
+
+            # Rescale and perform action
+            clipped_actions = actions
+
+            if isinstance(self.action_space, spaces.Box):
+                if self.policy.squash_output:
+                    # Unscale the actions to match env bounds
+                    # if they were previously squashed (scaled in [-1, 1])
+                    clipped_actions = self.policy.unscale_action(clipped_actions)
+                else:
+                    # Otherwise, clip the actions to avoid out of bound error
+                    # as we are sampling from an unbounded Gaussian distribution
+                    clipped_actions = np.clip(
+                        actions, self.action_space.low, self.action_space.high
+                    )
+
+            new_obs, rewards, dones, infos = env.step(clipped_actions)
+
+            self.num_timesteps += env.num_envs
+
+            # Give access to local variables
+            callback.update_locals(locals())
+            if not callback.on_step():
+                return False
+
+            self._update_info_buffer(infos, dones)
+            n_steps += 1
+
+            if isinstance(self.action_space, spaces.Discrete):
+                # Reshape in case of discrete action
+                actions = actions.reshape(-1, 1)
+
+            # Handle timeout by bootstraping with value function
+            # see GitHub issue #633
+            for idx, done in enumerate(dones):
+                if (
+                    done
+                    and infos[idx].get("terminal_observation") is not None
+                    and infos[idx].get("TimeLimit.truncated", False)
+                ):
+                    terminal_obs = self.policy.obs_to_tensor(
+                        infos[idx]["terminal_observation"]
+                    )[0]
+                    with th.no_grad():
+                        terminal_value = self.policy.predict_values(terminal_obs)[0]  # type: ignore[arg-type]
+                    rewards[idx] += self.gamma * terminal_value
+
+            rollout_buffer.add(
+                self._last_obs,  # type: ignore[arg-type]
+                actions,
+                rewards,
+                self._last_episode_starts,  # type: ignore[arg-type]
+                values,
+                log_probs,
+            )
+            self._last_obs = new_obs  # type: ignore[assignment]
+            self._last_episode_starts = dones
+
+        with th.no_grad():
+            # Compute value for the last timestep
+            if isinstance(new_obs[0], spaces.GraphInstance):
+                obs_tensor = graph_obs_to_thg_data(new_obs[0], device=self.device)
+            else:
+                obs_tensor = obs_as_tensor(new_obs, self.device)
+            values = self.policy.predict_values(obs_tensor)  # type: ignore[arg-type]
+
+        rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
+
+        callback.update_locals(locals())
+
+        callback.on_rollout_end()
+
+        return True
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/policies.py b/skdecide/hub/solver/stable_baselines/gnn/common/policies.py
new file mode 100644
index 0000000000..fcb6279fd3
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/policies.py
@@ -0,0 +1,112 @@
+import warnings
+from typing import Any, Optional, Tuple, Union
+
+import gymnasium as gym
+import torch as th
+import torch_geometric as thg
+from stable_baselines3.common.distributions import Distribution
+from stable_baselines3.common.policies import ActorCriticPolicy
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
+from stable_baselines3.common.type_aliases import Schedule
+
+from .torch_layers import GraphFeaturesExtractor
+from .utils import graph_obs_to_thg_data
+
+PyTorchGraphObs = Union[thg.data.Data, list[thg.data.Data]]
+
+
+class GNNActorCriticPolicy(ActorCriticPolicy):
+    def __init__(
+        self,
+        observation_space: gym.spaces.Graph,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[list[Union[int, dict[str, list[int]]]]] = None,
+        activation_fn: type[th.nn.Module] = th.nn.Tanh,
+        ortho_init: bool = True,
+        use_sde: bool = False,
+        log_std_init: float = 0.0,
+        full_std: bool = True,
+        use_expln: bool = False,
+        squash_output: bool = False,
+        features_extractor_class: type[BaseFeaturesExtractor] = GraphFeaturesExtractor,
+        features_extractor_kwargs: Optional[dict[str, Any]] = None,
+        share_features_extractor: bool = True,
+        normalize_images: bool = True,
+        optimizer_class: type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[dict[str, Any]] = None,
+    ):
+        super().__init__(
+            observation_space=observation_space,
+            action_space=action_space,
+            lr_schedule=lr_schedule,
+            net_arch=net_arch,
+            activation_fn=activation_fn,
+            ortho_init=ortho_init,
+            use_sde=use_sde,
+            log_std_init=log_std_init,
+            full_std=full_std,
+            use_expln=use_expln,
+            squash_output=squash_output,
+            features_extractor_class=features_extractor_class,
+            features_extractor_kwargs=features_extractor_kwargs,
+            share_features_extractor=share_features_extractor,
+            normalize_images=normalize_images,
+            optimizer_class=optimizer_class,
+            optimizer_kwargs=optimizer_kwargs,
+        )
+
+    def extract_features(
+        self,
+        obs: thg.data.Data,
+        features_extractor: Optional[BaseFeaturesExtractor] = None,
+    ) -> Union[th.Tensor, Tuple[th.Tensor, th.Tensor]]:
+        """
+        Preprocess the observation if needed and extract features.
+
+        :param obs: Observation
+        :param features_extractor: The features extractor to use. If None, then ``self.features_extractor`` is used.
+        :return: The extracted features. If features extractor is not shared, returns a tuple with the
+            features for the actor and the features for the critic.
+        """
+        if self.share_features_extractor:
+            if features_extractor is None:
+                features_extractor = self.features_extractor
+            return features_extractor(obs)
+        else:
+            if features_extractor is not None:
+                warnings.warn(
+                    "Provided features_extractor will be ignored because the features extractor is not shared.",
+                    UserWarning,
+                )
+
+            pi_features = self.pi_features_extractor(obs)
+            vf_features = self.vf_features_extractor(obs)
+            return pi_features, vf_features
+
+    def obs_to_tensor(
+        self, observation: gym.spaces.GraphInstance
+    ) -> tuple[PyTorchGraphObs, bool]:
+        if isinstance(observation, list):
+            vectorized_env = True
+        else:
+            vectorized_env = False
+        if vectorized_env:
+            torch_obs = [
+                graph_obs_to_thg_data(obs, device=self.device) for obs in observation
+            ]
+            if len(torch_obs) == 1:
+                torch_obs = torch_obs[0]
+        else:
+            torch_obs = graph_obs_to_thg_data(observation, device=self.device)
+        return torch_obs, vectorized_env
+
+    def get_distribution(self, obs: thg.data.Data) -> Distribution:
+        features = self.pi_features_extractor(obs)
+        latent_pi = self.mlp_extractor.forward_actor(features)
+        return self._get_action_dist_from_latent(latent_pi)
+
+    def predict_values(self, obs: thg.data.Data) -> th.Tensor:
+        features = self.vf_features_extractor(obs)
+        latent_vf = self.mlp_extractor.forward_critic(features)
+        return self.value_net(latent_vf)
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/torch_layers.py b/skdecide/hub/solver/stable_baselines/gnn/common/torch_layers.py
new file mode 100644
index 0000000000..0ff5943079
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/torch_layers.py
@@ -0,0 +1,115 @@
+from typing import Any, Optional
+
+import gymnasium as gym
+import numpy as np
+import torch as th
+import torch_geometric as thg
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
+from torch import nn
+from torch_geometric.nn import global_max_pool
+
+
+class GraphFeaturesExtractor(BaseFeaturesExtractor):
+    """Graph feature extractor for Graph observation spaces.
+
+    Will chain a gnn with a reduction layer to extract a fixed number of features.
+    The user can specify both the gnn and reduction layer.
+
+    By default, we use:
+    - gnn: a 2-layers GCN
+    - reduction layer: global_max_pool + linear layer + relu
+
+    Args:
+        observation_space:
+        features_dim: Number of extracted features
+            - If reduction_layer_class is given, should match the output of this network.
+            - If reduction_layer is None, will be used by the default network as its output dimension.
+        gnn_out_dim: dimension of the node embedding in gnn output
+            - If gnn is given, should not be None and should match the output of gnn
+            - If gnn is not given, will be used to generate it. By default, gnn_out_dim = 2 * features_dim
+        gnn_class: GNN network class (for instance chosen from `torch_geometric.nn.models` used to embed the graph observations)
+        gnn_kwargs: used by `gnn_class.__init__()`. Without effect if `gnn_class` is None.
+        reduction_layer_class: network class to be plugged after the gnn to get a fixed number of features.
+        reduction_layer_kwargs: used by `reduction_layer_class.__init__()`. Without effect if `reduction_layer_class` is None.
+
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Graph,
+        features_dim: int = 64,
+        gnn_out_dim: Optional[int] = None,
+        gnn_class: Optional[type[nn.Module]] = None,
+        gnn_kwargs: Optional[dict[str, Any]] = None,
+        reduction_layer_class: Optional[type[nn.Module]] = None,
+        reduction_layer_kwargs: Optional[dict[str, Any]] = None,
+    ):
+
+        super().__init__(observation_space, features_dim=features_dim)
+
+        if gnn_out_dim is None:
+            if gnn_class is None:
+                gnn_out_dim = 2 * features_dim
+            else:
+                raise ValueError(
+                    "`gnn_out_dim` cannot be None if `gnn` is not None, "
+                    "and should match `gnn` output."
+                )
+
+        if gnn_class is None:
+            node_features_dim = int(np.prod(observation_space.node_space.shape))
+            self.gnn = thg.nn.models.GCN(
+                in_channels=node_features_dim,
+                hidden_channels=gnn_out_dim,
+                num_layers=2,
+                dropout=0.2,
+            )
+        else:
+            if gnn_kwargs is None:
+                gnn_kwargs = {}
+            self.gnn = gnn_class(**gnn_kwargs)
+
+        if reduction_layer_class is None:
+            self.reduction_layer = _DefaultReductionLayer(
+                gnn_out_dim=gnn_out_dim, features_dim=features_dim
+            )
+        else:
+            if reduction_layer_kwargs is None:
+                reduction_layer_kwargs = {}
+            self.reduction_layer = reduction_layer_class(**reduction_layer_kwargs)
+
+    def forward(self, observations: thg.data.Data) -> th.Tensor:
+        x, edge_index, edge_attr, batch = (
+            observations.x,
+            observations.edge_index,
+            observations.edge_attr,
+            observations.batch,
+        )
+        # construct edge weights, for GNNs needing it, as the first edge feature
+        edge_weight = edge_attr[:, 0]
+        h = self.gnn(
+            x=x, edge_index=edge_index, edge_weight=edge_weight, edge_attr=edge_attr
+        )
+        embedded_observations = thg.data.Data(
+            x=h, edge_index=edge_index, edge_attr=edge_attr, batch=batch
+        )
+        h = self.reduction_layer(embedded_observations=embedded_observations)
+        return h
+
+
+class _DefaultReductionLayer(nn.Module):
+    def __init__(self, gnn_out_dim: int, features_dim: int):
+        super().__init__()
+        self.gnn_out_dim = gnn_out_dim
+        self.features_dim = features_dim
+        self.linear_layer = nn.Linear(gnn_out_dim, features_dim)
+
+    def forward(self, embedded_observations: thg.data.Data) -> th.Tensor:
+        x, edge_index, batch = (
+            embedded_observations.x,
+            embedded_observations.edge_index,
+            embedded_observations.batch,
+        )
+        h = global_max_pool(x, batch)
+        h = self.linear_layer(h).relu()
+        return h
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/utils.py b/skdecide/hub/solver/stable_baselines/gnn/common/utils.py
new file mode 100644
index 0000000000..b59e979217
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/utils.py
@@ -0,0 +1,34 @@
+import gymnasium as gym
+import torch as th
+import torch_geometric as thg
+
+
+def copy_graph_instance(g: gym.spaces.GraphInstance) -> gym.spaces.GraphInstance:
+    return gym.spaces.GraphInstance(
+        nodes=np.copy(g.nodes), edges=np.copy(g.edges), edge_links=np.copy(g.edge_links)
+    )
+
+
+def copy_np_array_or_list_of_graph_instances(
+    obs: Union[np.ndarray, list[gym.spaces.GraphInstance]]
+) -> Union[np.ndarray, list[gym.spaces.GraphInstance]]:
+    if isinstance(obs[0], gym.spaces.GraphInstance):
+        return [copy_graph_instance(g) for g in obs]
+    else:
+        return np.copy(obs)
+
+
+def graph_obs_to_thg_data(
+    obs: gym.spaces.GraphInstance, device: th.device
+) -> thg.data.Data:
+    # Node features
+    flatten_node_features = obs.nodes.reshape((len(obs.nodes), -1))
+    x = th.tensor(flatten_node_features).float()
+    # Edge features
+    if obs.edges is None:
+        edge_attr = None
+    else:
+        flatten_edge_features = obs.edges.reshape((len(obs.edges), -1))
+        edge_attr = th.tensor(flatten_edge_features).float()
+    edge_index = th.tensor(obs.edge_links, dtype=th.long).t().contiguous().view(2, -1)
+    return thg.data.Data(x=x, edge_index=edge_index, edge_attr=edge_attr).to(device)
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/vec_env/__init__.py b/skdecide/hub/solver/stable_baselines/gnn/common/vec_env/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/skdecide/hub/solver/stable_baselines/gnn/common/vec_env/dummy_vec_env.py b/skdecide/hub/solver/stable_baselines/gnn/common/vec_env/dummy_vec_env.py
new file mode 100644
index 0000000000..d5e22aa809
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/common/vec_env/dummy_vec_env.py
@@ -0,0 +1,71 @@
+from collections import OrderedDict
+from typing import Callable, List, Union
+
+import gymnasium as gym
+import numpy as np
+from stable_baselines3.common.env_util import is_wrapped
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.type_aliases import GymEnv
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
+from stable_baselines3.common.vec_env.util import dict_to_obs
+
+from ..utils import copy_np_array_or_list_of_graph_instances
+
+EnvSubObs = Union[np.ndarray, list[gym.spaces.GraphInstance]]
+VecEnvObs = Union[EnvSubObs, dict[str, EnvSubObs], tuple[EnvSubObs, ...]]
+
+
+class GraphDummyVecEnv(DummyVecEnv):
+    def __init__(self, env_fns: List[Callable[[], gym.Env]]):
+        super().__init__(env_fns)
+        # replace buffers for graph spaces by lists
+        obs_space = self.envs[0].observation_space
+        if isinstance(obs_space, gym.spaces.Graph):
+            self.buf_obs[None] = [None for _ in range(self.num_envs)]
+        elif isinstance(obs_space, gym.spaces.Dict):
+            for k, space in obs_space.spaces:
+                if isinstance(space, gym.spaces.Graph):
+                    self.buf_obs[k] = [None for _ in range(self.num_envs)]
+
+    def _obs_from_buf(self) -> VecEnvObs:
+        return dict_to_obs(self.observation_space, copy_obs_dict(self.buf_obs))
+
+
+def copy_obs_dict(obs: dict[str, EnvSubObs]) -> dict[str, EnvSubObs]:
+    """
+    Deep-copy a dict of numpy arrays.
+
+    :param obs: a dict of numpy arrays.
+    :return: a dict of copied numpy arrays.
+    """
+    assert isinstance(
+        obs, OrderedDict
+    ), f"unexpected type for observations '{type(obs)}'"
+    return OrderedDict(
+        [(k, copy_np_array_or_list_of_graph_instances(v)) for k, v in obs.items()]
+    )
+
+
+def wrap_graph_env(
+    env: GymEnv, verbose: int = 0, monitor_wrapper: bool = True
+) -> VecEnv:
+    """Wrap environment with the appropriate wrappers if needed.
+
+    :param env:
+    :param verbose: Verbosity level: 0 for no output, 1 for indicating wrappers used
+    :param monitor_wrapper: Whether to wrap the env in a ``Monitor`` when possible.
+    :return: The wrapped environment.
+    """
+    if not isinstance(env, VecEnv):
+        if not is_wrapped(env, Monitor) and monitor_wrapper:
+            if verbose >= 1:
+                print("Wrapping the env with a `Monitor` wrapper")
+            env = Monitor(env)
+        if verbose >= 1:
+            print("Wrapping the env in a DummyVecEnv.")
+        # patch: add dummy shape and dtype to graph obs space to avoid issues
+        env.observation_space._shape = (0,)
+        env.observation_space.dtype = np.float_
+        env = GraphDummyVecEnv([lambda: env])  # type: ignore[list-item, return-value]
+    return env
diff --git a/skdecide/hub/solver/stable_baselines/gnn/ppo/__init__.py b/skdecide/hub/solver/stable_baselines/gnn/ppo/__init__.py
new file mode 100644
index 0000000000..500a814e2d
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/ppo/__init__.py
@@ -0,0 +1,4 @@
+from ..common.policies import GNNActorCriticPolicy
+from .ppo import GraphPPO
+
+GraphInputPolicy = GNNActorCriticPolicy
diff --git a/skdecide/hub/solver/stable_baselines/gnn/ppo/ppo.py b/skdecide/hub/solver/stable_baselines/gnn/ppo/ppo.py
new file mode 100644
index 0000000000..c5a0af089d
--- /dev/null
+++ b/skdecide/hub/solver/stable_baselines/gnn/ppo/ppo.py
@@ -0,0 +1,13 @@
+from typing import ClassVar
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.policies import BasePolicy
+
+from ..common.on_policy_algorithm import GraphOnPolicyAlgorithm
+from ..common.policies import GNNActorCriticPolicy
+
+
+class GraphPPO(GraphOnPolicyAlgorithm, PPO):
+    policy_aliases: ClassVar[dict[str, type[BasePolicy]]] = {
+        "GraphInputPolicy": GNNActorCriticPolicy,
+    }
diff --git a/skdecide/hub/solver/stable_baselines/stable_baselines.py b/skdecide/hub/solver/stable_baselines/stable_baselines.py
index 7f4cfa7daf..9af1d5a0bb 100644
--- a/skdecide/hub/solver/stable_baselines/stable_baselines.py
+++ b/skdecide/hub/solver/stable_baselines/stable_baselines.py
@@ -17,7 +17,6 @@
 from stable_baselines3.common.callbacks import BaseCallback, ConvertCallback
 from stable_baselines3.common.policies import BasePolicy
 from stable_baselines3.common.type_aliases import MaybeCallback
-from stable_baselines3.common.vec_env import DummyVecEnv
 
 from skdecide import Domain, Solver
 from skdecide.builders.domain import (
@@ -146,9 +145,7 @@ def _solve(self) -> None:
             self, "_algo"
         ):  # reuse algo if possible (enables further learning)
             domain = self._domain_factory()
-            env = DummyVecEnv(
-                [lambda: AsGymnasiumEnv(domain)]
-            )  # the algorithms require a vectorized environment to run
+            env = AsGymnasiumEnv(domain)  # we let the algo wrap it in a vectorized env
             self._algo = self._algo_class(
                 self._baselines_policy, env, **self._algo_kwargs
             )
@@ -182,8 +179,7 @@ def _save(self, path: str) -> None:
 
     def _load(self, path: str):
         domain = self._domain_factory()
-        env = DummyVecEnv([lambda: AsGymnasiumEnv(domain)])
-        self._algo = self._algo_class.load(path, env=env)
+        self._algo = self._algo_class.load(path, env=AsGymnasiumEnv(domain))
         self._init_algo(domain)
 
     def _init_algo(self, domain: D):
diff --git a/tests/solvers/python/test_gnn_sb3.py b/tests/solvers/python/test_gnn_sb3.py
new file mode 100644
index 0000000000..2e4715c0b8
--- /dev/null
+++ b/tests/solvers/python/test_gnn_sb3.py
@@ -0,0 +1,417 @@
+import sys
+from typing import Any, Optional
+
+import numpy as np
+import pytest
+import torch as th
+import torch_geometric as thg
+from gymnasium.spaces import Box, Discrete, Graph, GraphInstance
+from pytest_cases import fixture, fixture_union, param_fixture
+from torch_geometric.nn import global_add_pool
+
+from skdecide.builders.domain import Renderable, UnrestrictedActions
+from skdecide.core import Space, TransitionOutcome, Value
+from skdecide.domains import DeterministicPlanningDomain, Domain
+from skdecide.hub.domain.gym import GymDomain
+from skdecide.hub.domain.maze import Maze
+from skdecide.hub.domain.maze.maze import DEFAULT_MAZE, Action, State
+from skdecide.hub.solver.stable_baselines import StableBaseline
+from skdecide.hub.solver.stable_baselines.gnn import GraphPPO
+from skdecide.hub.solver.stable_baselines.gnn.common.torch_layers import (
+    GraphFeaturesExtractor,
+)
+from skdecide.hub.space.gym import GymSpace, ListSpace
+from skdecide.utils import rollout
+
+if not sys.platform.startswith("win"):
+    try:
+        from graph_jsp_env.disjunctive_graph_jsp_env import DisjunctiveGraphJspEnv
+    except ImportError:
+        graph_jsp_env_available = False
+    else:
+        graph_jsp_env_available = True
+else:
+    # import not working on windows because of the banner
+    graph_jsp_env_available = False
+
+
+if graph_jsp_env_available:
+    # JSP graph env
+
+    class D(Domain):
+        T_state = GraphInstance  # Type of states
+        T_observation = T_state  # Type of observations
+        T_event = int  # Type of events
+        T_value = float  # Type of transition values (rewards or costs)
+        T_info = None  # Type of additional information in environment outcome
+
+    class GraphJspDomain(GymDomain, D):
+        _gym_env: DisjunctiveGraphJspEnv
+
+        def __init__(self, gym_env):
+            GymDomain.__init__(self, gym_env=gym_env)
+            if self._gym_env.normalize_observation_space:
+                self.n_nodes_features = gym_env.n_machines + 1
+            else:
+                self.n_nodes_features = 2
+
+        def _state_step(
+            self, action: D.T_event
+        ) -> TransitionOutcome[D.T_state, Value[D.T_value], D.T_predicate, D.T_info]:
+            outcome = super()._state_step(action=action)
+            outcome.state = self._np_state2graph_state(outcome.state)
+            return outcome
+
+        def _get_applicable_actions_from(
+            self, memory: D.T_memory[D.T_state]
+        ) -> D.T_agent[Space[D.T_event]]:
+            return ListSpace(np.nonzero(self._gym_env.valid_action_mask())[0])
+
+        def _is_applicable_action_from(
+            self, action: D.T_agent[D.T_event], memory: D.T_memory[D.T_state]
+        ) -> bool:
+            return self._gym_env.valid_action_mask()[action]
+
+        def _state_reset(self) -> D.T_state:
+            return self._np_state2graph_state(super()._state_reset())
+
+        def _get_observation_space_(self) -> Space[D.T_observation]:
+            if self._gym_env.normalize_observation_space:
+                original_graph_space = Graph(
+                    node_space=Box(
+                        low=0.0,
+                        high=1.0,
+                        shape=(self.n_nodes_features,),
+                        dtype=np.float_,
+                    ),
+                    edge_space=Box(low=0, high=1.0, dtype=np.float_),
+                )
+
+            else:
+                original_graph_space = Graph(
+                    node_space=Box(
+                        low=np.array([0, 0]),
+                        high=np.array(
+                            [
+                                self._gym_env.n_machines,
+                                self._gym_env.longest_processing_time,
+                            ]
+                        ),
+                        dtype=np.int_,
+                    ),
+                    edge_space=Box(
+                        low=0, high=self._gym_env.longest_processing_time, dtype=np.int_
+                    ),
+                )
+            return GymSpace(original_graph_space)
+
+        def _np_state2graph_state(self, np_state: np.array) -> GraphInstance:
+            if not self._gym_env.normalize_observation_space:
+                np_state = np_state.astype(np.int_)
+
+            nodes = np_state[:, -self.n_nodes_features :]
+            adj = np_state[:, : -self.n_nodes_features]
+            edge_starts_ends = adj.nonzero()
+            edge_links = np.transpose(edge_starts_ends)
+            edges = adj[edge_starts_ends][:, None]
+
+            return GraphInstance(nodes=nodes, edges=edges, edge_links=edge_links)
+
+        def _render_from(self, memory: D.T_memory[D.T_state], **kwargs: Any) -> Any:
+            return self._gym_env.render(**kwargs)
+
+    jsp = np.array(
+        [
+            [
+                [0, 1, 2],  # machines for job 0
+                [0, 2, 1],  # machines for job 1
+                [0, 1, 2],  # machines for job 2
+            ],
+            [
+                [3, 2, 2],  # task durations of job 0
+                [2, 1, 4],  # task durations of job 1
+                [0, 4, 3],  # task durations of job 2
+            ],
+        ]
+    )
+
+
+class D(DeterministicPlanningDomain, UnrestrictedActions, Renderable):
+    T_state = GraphInstance  # Type of states
+    T_observation = T_state  # Type of observations
+    T_event = Action  # Type of events
+    T_value = float  # Type of transition values (rewards or costs)
+    T_predicate = bool  # Type of logical checks
+    T_info = (
+        None  # Type of additional information given as part of an environment outcome
+    )
+
+
+class GraphMaze(D):
+    def __init__(self, maze_str: str = DEFAULT_MAZE, discrete_features: bool = False):
+        self.discrete_features = discrete_features
+        self.maze_domain = Maze(maze_str=maze_str)
+        np_wall = np.array(self.maze_domain._maze)
+        np_y = np.array(
+            [
+                [(i) for j in range(self.maze_domain._num_cols)]
+                for i in range(self.maze_domain._num_rows)
+            ]
+        )
+        np_x = np.array(
+            [
+                [(j) for j in range(self.maze_domain._num_cols)]
+                for i in range(self.maze_domain._num_rows)
+            ]
+        )
+        walls = np_wall.ravel()
+        coords = [i for i in zip(np_y.ravel(), np_x.ravel())]
+        np_node_id = np.reshape(range(len(walls)), np_wall.shape)
+        edge_links = []
+        edges = []
+        for i in range(self.maze_domain._num_rows):
+            for j in range(self.maze_domain._num_cols):
+                current_coord = (i, j)
+                if i > 0:
+                    next_coord = (i - 1, j)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if i < self.maze_domain._num_rows - 1:
+                    next_coord = (i + 1, j)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if j > 0:
+                    next_coord = (i, j - 1)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+                if j < self.maze_domain._num_cols - 1:
+                    next_coord = (i, j + 1)
+                    edge_links.append(
+                        (np_node_id[current_coord], np_node_id[next_coord])
+                    )
+                    edges.append(np_wall[current_coord] * np_wall[next_coord])
+        self.edges = np.array(edges)
+        self.edge_links = np.array(edge_links)
+        self.walls = walls
+        self.node_ids = np_node_id
+        self.coords = coords
+
+    def _mazestate2graph(self, state: State) -> GraphInstance:
+        x, y = state
+        agent_presence = np.zeros(self.walls.shape, dtype=self.walls.dtype)
+        agent_presence[self.node_ids[y, x]] = 1
+        nodes = np.stack([self.walls, agent_presence], axis=-1)
+        if self.discrete_features:
+            return GraphInstance(
+                nodes=nodes, edges=self.edges, edge_links=self.edge_links
+            )
+        else:
+            return GraphInstance(
+                nodes=nodes, edges=self.edges[:, None], edge_links=self.edge_links
+            )
+
+    def _graph2mazestate(self, graph: GraphInstance) -> State:
+        y, x = self.coords[graph.nodes[:, 1].nonzero()[0][0]]
+        return State(x=x, y=y)
+
+    def _is_terminal(self, state: D.T_state) -> D.T_predicate:
+        return self.maze_domain._is_terminal(self._graph2mazestate(state))
+
+    def _get_next_state(self, memory: D.T_state, action: D.T_event) -> D.T_state:
+        maze_memory = self._graph2mazestate(memory)
+        maze_next_state = self.maze_domain._get_next_state(
+            memory=maze_memory, action=action
+        )
+        return self._mazestate2graph(maze_next_state)
+
+    def _get_transition_value(
+        self,
+        memory: D.T_state,
+        action: D.T_event,
+        next_state: Optional[D.T_state] = None,
+    ) -> Value[D.T_value]:
+        maze_memory = self._graph2mazestate(memory)
+        if next_state is None:
+            maze_next_state = None
+        else:
+            maze_next_state = self._graph2mazestate(next_state)
+        return self.maze_domain._get_transition_value(
+            memory=maze_memory, action=action, next_state=maze_next_state
+        )
+
+    def _get_action_space_(self) -> Space[D.T_event]:
+        return self.maze_domain._get_action_space_()
+
+    def _get_goals_(self) -> Space[D.T_observation]:
+        return ListSpace([self._mazestate2graph(self.maze_domain._goal)])
+
+    def _is_goal(
+        self, observation: D.T_agent[D.T_observation]
+    ) -> D.T_agent[D.T_predicate]:
+        return self.maze_domain._is_goal(self._graph2mazestate(observation))
+
+    def _get_initial_state_(self) -> D.T_state:
+        return self._mazestate2graph(self.maze_domain._get_initial_state_())
+
+    def _get_observation_space_(self) -> Space[D.T_observation]:
+        if self.discrete_features:
+            return GymSpace(
+                Graph(
+                    node_space=Box(low=0, high=1, shape=(2,), dtype=self.walls.dtype),
+                    edge_space=Discrete(2),
+                )
+            )
+        else:
+            return GymSpace(
+                Graph(
+                    node_space=Box(low=0, high=1, shape=(2,), dtype=self.walls.dtype),
+                    edge_space=Box(low=0, high=1, shape=(1,), dtype=self.edges.dtype),
+                )
+            )
+
+    def _render_from(self, memory: D.T_state, **kwargs: Any) -> Any:
+        maze_memory = self._graph2mazestate(memory)
+        self.maze_domain._render_from(memory=maze_memory, **kwargs)
+
+
+discrete_features = param_fixture("discrete_features", [False, True])
+
+
+@fixture
+def maze_domain_factory(discrete_features):
+    return lambda: GraphMaze(discrete_features=discrete_features)
+
+
+@fixture
+def jsp_domain_factory():
+    if sys.platform.startswith("win"):
+        pytest.skip("jsp-graph-env not importable on windows")
+    if not graph_jsp_env_available:
+        pytest.skip("jsp-graph-env not available")
+    return lambda: GraphJspDomain(
+        gym_env=DisjunctiveGraphJspEnv(
+            jps_instance=jsp,
+            perform_left_shift_if_possible=True,
+            normalize_observation_space=False,
+            flat_observation_space=False,
+            action_mode="task",
+        )
+    )
+
+
+domain_factory = fixture_union(
+    "domain_factory", [maze_domain_factory, jsp_domain_factory]
+)
+
+
+def test_observation_space(domain_factory):
+    domain = domain_factory()
+    assert domain.reset() in domain.get_observation_space()
+
+
+def test_ppo(domain_factory):
+    with StableBaseline(
+        domain_factory=domain_factory,
+        algo_class=GraphPPO,
+        baselines_policy="GraphInputPolicy",
+        learn_config={"total_timesteps": 100},
+    ) as solver:
+
+        solver.solve()
+        rollout(
+            domain=domain_factory(),
+            solver=solver,
+            max_steps=100,
+            num_episodes=1,
+            render=False,
+        )
+
+
+def test_ppo_user_gnn(domain_factory):
+    domain = domain_factory()
+    node_features_dim = int(
+        np.prod(domain.get_observation_space().unwrapped().node_space.shape)
+    )
+    with StableBaseline(
+        domain_factory=domain_factory,
+        algo_class=GraphPPO,
+        baselines_policy="GraphInputPolicy",
+        learn_config={"total_timesteps": 100},
+        policy_kwargs=dict(
+            features_extractor_class=GraphFeaturesExtractor,
+            features_extractor_kwargs=dict(
+                gnn_class=thg.nn.models.GAT,
+                gnn_kwargs=dict(
+                    in_channels=node_features_dim,
+                    hidden_channels=64,
+                    num_layers=2,
+                    dropout=0.2,
+                ),
+                gnn_out_dim=64,
+                features_dim=64,
+            ),
+        ),
+    ) as solver:
+
+        solver.solve()
+        rollout(
+            domain=domain_factory(),
+            solver=solver,
+            max_steps=100,
+            num_episodes=1,
+            render=False,
+        )
+
+
+class MyReductionLayer(th.nn.Module):
+    def __init__(self, gnn_out_dim: int, features_dim: int):
+        super().__init__()
+        self.gnn_out_dim = gnn_out_dim
+        self.features_dim = features_dim
+        self.linear_layer = th.nn.Linear(gnn_out_dim, features_dim)
+
+    def forward(self, embedded_observations: thg.data.Data) -> th.Tensor:
+        x, edge_index, batch = (
+            embedded_observations.x,
+            embedded_observations.edge_index,
+            embedded_observations.batch,
+        )
+        h = global_add_pool(x, batch)
+        h = self.linear_layer(h).relu()
+        return h
+
+
+def test_ppo_user_reduction_layer(domain_factory):
+    with StableBaseline(
+        domain_factory=domain_factory,
+        algo_class=GraphPPO,
+        baselines_policy="GraphInputPolicy",
+        learn_config={"total_timesteps": 100},
+        policy_kwargs=dict(
+            features_extractor_class=GraphFeaturesExtractor,
+            features_extractor_kwargs=dict(
+                gnn_out_dim=128,
+                features_dim=64,
+                reduction_layer_class=MyReductionLayer,
+                reduction_layer_kwargs=dict(
+                    gnn_out_dim=128,
+                    features_dim=64,
+                ),
+            ),
+        ),
+    ) as solver:
+
+        solver.solve()
+        rollout(
+            domain=domain_factory(),
+            solver=solver,
+            max_steps=100,
+            num_episodes=1,
+            render=False,
+        )