[Environment] MAgent2

.github/unittest/install_magent2.sh

@@ -0,0 +1,6 @@
+
+
+pip install git+https://github.com/Farama-Foundation/MAgent2
+
+sudo apt-get update
+sudo apt-get install python3-opengl xvfb

.github/workflows/magent_tests.yml

@@ -0,0 +1,43 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see:
+# https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+
+name: magent_tests
+
+on:
+  push:
+    branches: [ $default-branch , "main" ]
+  pull_request:
+    branches: [ $default-branch , "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+       bash .github/unittest/install_dependencies_nightly.sh
+    - name: Install magent2
+      run: |
+       bash .github/unittest/install_magent2.sh
+    - name: Test with pytest
+      run: |
+        xvfb-run -s "-screen 0 1024x768x24" pytest test/test_magent.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
+    - name: Upload coverage to Codecov
+      uses: codecov/codecov-action@v3
+      with:
+        fail_ci_if_error: false

README.md

@@ -116,6 +116,13 @@ pip install "pettingzoo[all]"
 ```bash
 pip install dm-meltingpot
 ```
+
+##### MAgent2
+
+```bash
+pip install git+https://github.com/Farama-Foundation/MAgent
+```
+
 ##### SMACv2
 
 Follow the instructions on the environment [repository](https://github.com/oxwhirl/smacv2).
@@ -239,13 +246,14 @@ determine the training strategy. Here is a table with the currently implemented
 challenge to solve.
 They differ based on many aspects, here is a table with the current environments in BenchMARL
 
-| Environment                                                        | Tasks                                | Cooperation               | Global state | Reward function               | Action space          |    Vectorized    |
-|--------------------------------------------------------------------|--------------------------------------|---------------------------|--------------|-------------------------------|-----------------------|:----------------:|
-| [VMAS](https://github.com/proroklab/VectorizedMultiAgentSimulator) | [27](benchmarl/conf/task/vmas)       | Cooperative + Competitive | No           | Shared + Independent + Global | Continuous + Discrete |       Yes        |    
-| [SMACv2](https://github.com/oxwhirl/smacv2)                        | [15](benchmarl/conf/task/smacv2)     | Cooperative               | Yes          | Global                        | Discrete              |        No        |
-| [MPE](https://github.com/openai/multiagent-particle-envs)          | [8](benchmarl/conf/task/pettingzoo)  | Cooperative + Competitive | Yes          | Shared + Independent          | Continuous + Discrete |        No        |
-| [SISL](https://github.com/sisl/MADRL)                              | [2](benchmarl/conf/task/pettingzoo)  | Cooperative               | No           | Shared                        | Continuous            |        No        |
-| [MeltingPot](https://github.com/google-deepmind/meltingpot)        | [49](benchmarl/conf/task/meltingpot) | Cooperative + Competitive | Yes          | Independent                   | Discrete              |        No        |
+| Environment                                                         | Tasks                                | Cooperation               | Global state | Reward function               | Action space          |    Vectorized    |
+|---------------------------------------------------------------------|--------------------------------------|---------------------------|--------------|-------------------------------|-----------------------|:----------------:|
+| [VMAS](https://github.com/proroklab/VectorizedMultiAgentSimulator)  | [27](benchmarl/conf/task/vmas)       | Cooperative + Competitive | No           | Shared + Independent + Global | Continuous + Discrete |       Yes        |    
+| [SMACv2](https://github.com/oxwhirl/smacv2)                         | [15](benchmarl/conf/task/smacv2)     | Cooperative               | Yes          | Global                        | Discrete              |        No        |
+| [MPE](https://github.com/openai/multiagent-particle-envs)           | [8](benchmarl/conf/task/pettingzoo)  | Cooperative + Competitive | Yes          | Shared + Independent          | Continuous + Discrete |        No        |
+| [SISL](https://github.com/sisl/MADRL)                               | [2](benchmarl/conf/task/pettingzoo)  | Cooperative               | No           | Shared                        | Continuous            |        No        |
+| [MeltingPot](https://github.com/google-deepmind/meltingpot)         | [49](benchmarl/conf/task/meltingpot) | Cooperative + Competitive | Yes          | Independent                   | Discrete              |        No        |
+| [MAgent2](https://github.com/Farama-Foundation/magent2)             | [1](benchmarl/conf/task/magent)      | Cooperative + Competitive | Yes          | Global in groups              | Discrete              |        No        |
 
 
 > [!NOTE]  

benchmarl/conf/task/magent/adversarial_pursuit.yaml

@@ -0,0 +1,9 @@
+defaults:
+  - magent_adversarial_pursuit_config
+  - _self_
+
+map_size: 45
+minimap_mode: False
+tag_penalty: -0.2
+max_cycles: 500
+extra_features: False

benchmarl/environments/__init__.py

@@ -5,14 +5,16 @@
 #
 
 from .common import _get_task_config_class, Task
+
+from .magent.common import MAgentTask
 from .meltingpot.common import MeltingPotTask
 from .pettingzoo.common import PettingZooTask
 from .smacv2.common import Smacv2Task
 from .vmas.common import VmasTask
 
 # The enum classes for the environments available.
 # This is the only object in this file you need to modify when adding a new environment.
-tasks = [VmasTask, Smacv2Task, PettingZooTask, MeltingPotTask]
+tasks = [VmasTask, Smacv2Task, PettingZooTask, MeltingPotTask, MAgentTask]
 
 # This is a registry mapping "envname/task_name" to the EnvNameTask.TASK_NAME enum
 # It is used by automatically load task enums from yaml files.

benchmarl/environments/magent/adversarial_pursuit.py

@@ -0,0 +1,16 @@
+#  Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+#  This source code is licensed under the license found in the
+#  LICENSE file in the root directory of this source tree.
+#
+
+from dataclasses import dataclass, MISSING
+
+
+@dataclass
+class TaskConfig:
+    map_size: int = MISSING
+    minimap_mode: bool = MISSING
+    tag_penalty: float = MISSING
+    max_cycles: int = MISSING
+    extra_features: bool = MISSING

benchmarl/environments/magent/common.py

@@ -0,0 +1,131 @@
+#  Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+#  This source code is licensed under the license found in the
+#  LICENSE file in the root directory of this source tree.
+#
+
+from typing import Callable, Dict, List, Optional
+
+from torchrl.data import Composite
+from torchrl.envs import EnvBase, PettingZooWrapper
+
+from benchmarl.environments.common import Task
+
+from benchmarl.utils import DEVICE_TYPING
+
+
+class MAgentTask(Task):
+    """Enum for MAgent2 tasks."""
+
+    ADVERSARIAL_PURSUIT = None
+    # BATTLE = None
+    # BATTLEFIELD = None
+    # COMBINED_ARMS = None
+    # GATHER = None
+    # TIGER_DEER = None
+
+    def get_env_fun(
+        self,
+        num_envs: int,
+        continuous_actions: bool,
+        seed: Optional[int],
+        device: DEVICE_TYPING,
+    ) -> Callable[[], EnvBase]:
+
+        return lambda: PettingZooWrapper(
+            env=self.__get_env(),
+            return_state=True,
+            seed=seed,
+            done_on_any=False,
+            use_mask=False,
+            device=device,
+        )
+
+    def __get_env(self) -> EnvBase:
+        try:
+            from magent2.environments import (
+                adversarial_pursuit_v4,
+                # battle_v4,
+                # battlefield_v5,
+                # combined_arms_v6,
+                # gather_v5,
+                # tiger_deer_v4
+            )
+        except ImportError:
+            raise ImportError(
+                "Module `magent2` not found, install it using `pip install magent2`"
+            )
+
+        envs = {
+            "ADVERSARIAL_PURSUIT": adversarial_pursuit_v4,
+            # "BATTLE": battle_v4,
+            # "BATTLEFIELD": battlefield_v5,
+            # "COMBINED_ARMS": combined_arms_v6,
+            # "GATHER": gather_v5,
+            # "TIGER_DEER": tiger_deer_v4
+        }
+        if self.name not in envs:
+            raise Exception(f"{self.name} is not an environment of MAgent2")
+        return envs[self.name].parallel_env(**self.config, render_mode="rgb_array")
+
+    def supports_continuous_actions(self) -> bool:
+        return False
+
+    def supports_discrete_actions(self) -> bool:
+        return True
+
+    def has_state(self) -> bool:
+        return True
+
+    def has_render(self, env: EnvBase) -> bool:
+        return True
+
+    def max_steps(self, env: EnvBase) -> int:
+        return self.config["max_cycles"]
+
+    def group_map(self, env: EnvBase) -> Dict[str, List[str]]:
+        return env.group_map
+
+    def state_spec(self, env: EnvBase) -> Optional[Composite]:
+        return Composite({"state": env.observation_spec["state"].clone()})
+
+    def action_mask_spec(self, env: EnvBase) -> Optional[Composite]:
+        observation_spec = env.observation_spec.clone()
+        for group in self.group_map(env):
+            group_obs_spec = observation_spec[group]
+            for key in list(group_obs_spec.keys()):
+                if key != "action_mask":
+                    del group_obs_spec[key]
+            if group_obs_spec.is_empty():
+                del observation_spec[group]
+        del observation_spec["state"]
+        if observation_spec.is_empty():
+            return None
+        return observation_spec
+
+    def observation_spec(self, env: EnvBase) -> Composite:
+        observation_spec = env.observation_spec.clone()
+        for group in self.group_map(env):
+            group_obs_spec = observation_spec[group]
+            for key in list(group_obs_spec.keys()):
+                if key != "observation":
+                    del group_obs_spec[key]
+        del observation_spec["state"]
+        return observation_spec
+
+    def info_spec(self, env: EnvBase) -> Optional[Composite]:
+        observation_spec = env.observation_spec.clone()
+        for group in self.group_map(env):
+            group_obs_spec = observation_spec[group]
+            for key in list(group_obs_spec.keys()):
+                if key != "info":
+                    del group_obs_spec[key]
+        del observation_spec["state"]
+        return observation_spec
+
+    def action_spec(self, env: EnvBase) -> Composite:
+        return env.full_action_spec
+
+    @staticmethod
+    def env_name() -> str:
+        return "magent"

docs/source/concepts/components.rst

@@ -91,6 +91,8 @@ They differ based on many aspects, here is a table with the current environments
     +-------------------------------------------------+-------+---------------------------+--------------+-------------------------------+-----------------------+------------+
     | :class:`~benchmarl.environments.MeltingPotTask` |  49   | Cooperative + Competitive |     Yes      |          Independent          |       Discrete        |     No     |
     +-------------------------------------------------+-------+---------------------------+--------------+-------------------------------+-----------------------+------------+
+    | :class:`~benchmarl.environments.MAgentTask`     |  1    | Cooperative + Competitive |     Yes      |          Global in groups     |       Discrete        |     No     |
+    +-------------------------------------------------+-------+---------------------------+--------------+-------------------------------+-----------------------+------------+
 
 
 

docs/source/usage/installation.rst

@@ -87,6 +87,16 @@ Follow the instructions on the environment `repository <https://github.com/oxwhi
 `Here <https://github.com/facebookresearch/BenchMARL/blob/main/.github/unittest/install_smacv2.sh>`_
 is how we install it on linux.
 
+MAgent2
+^^^^^^^
+:github:`null` `GitHub <https://github.com/Farama-Foundation/MAgent>`__
+
+
+.. code-block:: console
+
+   pip install git+https://github.com/Farama-Foundation/MAgent
+
+
 Install models
 --------------
 

test/conftest.py

@@ -90,6 +90,31 @@ def mlp_gnn_sequence_config() -> ModelConfig:
     )
 
 
+@pytest.fixture
+def cnn_gnn_sequence_config() -> ModelConfig:
+    return SequenceModelConfig(
+        model_configs=[
+            CnnConfig(
+                cnn_num_cells=[4, 3],
+                cnn_kernel_sizes=[3, 2],
+                cnn_strides=1,
+                cnn_paddings=0,
+                cnn_activation_class=nn.Tanh,
+                mlp_num_cells=[4],
+                mlp_activation_class=nn.Tanh,
+                mlp_layer_class=nn.Linear,
+            ),
+            GnnConfig(
+                topology="full",
+                self_loops=False,
+                gnn_class=torch_geometric.nn.conv.GATv2Conv,
+            ),
+            MlpConfig(num_cells=[4], activation_class=nn.Tanh, layer_class=nn.Linear),
+        ],
+        intermediate_sizes=[5, 3],
+    )
+
+
 @pytest.fixture
 def gru_mlp_sequence_config() -> ModelConfig:
     return SequenceModelConfig(
@@ -128,3 +153,32 @@ def lstm_mlp_sequence_config() -> ModelConfig:
         ],
         intermediate_sizes=[5],
     )
+
+
+@pytest.fixture
+def cnn_lstm_sequence_config() -> ModelConfig:
+    return SequenceModelConfig(
+        model_configs=[
+            CnnConfig(
+                cnn_num_cells=[4, 3],
+                cnn_kernel_sizes=[3, 2],
+                cnn_strides=1,
+                cnn_paddings=0,
+                cnn_activation_class=nn.Tanh,
+                mlp_num_cells=[4],
+                mlp_activation_class=nn.Tanh,
+                mlp_layer_class=nn.Linear,
+            ),
+            LstmConfig(
+                hidden_size=13,
+                mlp_num_cells=[],
+                mlp_activation_class=nn.Tanh,
+                mlp_layer_class=nn.Linear,
+                n_layers=1,
+                bias=True,
+                dropout=0,
+                compile=False,
+            ),
+        ],
+        intermediate_sizes=[5],
+    )