[Environment] Melitngpot

.github/unittest/install_meltingpot.sh

@@ -0,0 +1,2 @@
+
+pip install dm-meltingpot

.github/workflows/lint.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v3

.github/workflows/meltingpot_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: meltingpot_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 meltingpot
+      run: |
+       bash .github/unittest/install_meltingpot.sh
+    - name: Test with pytest
+      run: |
+        pytest test/test_meltingpot.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

.github/workflows/pettingzoo_tests.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -37,9 +37,7 @@ jobs:
     - name: Test with pytest
       run: |
         xvfb-run -s "-screen 0 1024x768x24" pytest test/test_pettingzoo.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
-
-    - if: matrix.python-version == '3.10'
-      name: Upload coverage to Codecov
+    - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v3
       with:
         fail_ci_if_error: false

.github/workflows/smacv2_tests.yml

@@ -21,7 +21,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -44,8 +44,7 @@ jobs:
 
         pytest test/test_smacv2.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
 
-    - if: matrix.python-version == '3.10'
-      name: Upload coverage to Codecov
+    - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v3
       with:
         fail_ci_if_error: false

.github/workflows/torchrl_stable_tests.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
@@ -38,4 +38,4 @@ jobs:
         bash .github/unittest/install_pettingzoo.sh
     - name: Tests
       run: |
-        xvfb-run -s "-screen 0 1024x768x24" pytest test/test_algorithm.py test/test_models.py test/test_task.py test/test_vmas.py test/test_pettingzoo.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
+        xvfb-run -s "-screen 0 1024x768x24" pytest test/test_algorithm.py test/test_models.py test/test_task.py test/test_vmas.py test/test_pettingzoo.py test/test_meltingpot.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html

.github/workflows/unit_tests.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10","3.11"]
 
     steps:
     - uses: actions/checkout@v3

.github/workflows/vmas_tests.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -38,8 +38,7 @@ jobs:
       run: |
         xvfb-run -s "-screen 0 1024x768x24" pytest test/test_vmas.py --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html
 
-    - if: matrix.python-version == '3.10'
-      name: Upload coverage to Codecov
+    - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v3
       with:
         fail_ci_if_error: false

README.md

@@ -5,7 +5,7 @@
 [![tests](https://github.com/facebookresearch/BenchMARL/actions/workflows/unit_tests.yml/badge.svg)](test)
 [![codecov](https://codecov.io/github/facebookresearch/BenchMARL/coverage.svg?branch=main)](https://codecov.io/gh/facebookresearch/BenchMARL)
 [![Documentation Status](https://readthedocs.org/projects/benchmarl/badge/?version=latest)](https://benchmarl.readthedocs.io/en/latest/?badge=latest)
-[![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://www.python.org/downloads/)
+[![Python](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue.svg)](https://www.python.org/downloads/)
 <a href="https://pypi.org/project/benchmarl"><img src="https://img.shields.io/pypi/v/benchmarl" alt="pypi version"></a>
 [![Downloads](https://static.pepy.tech/personalized-badge/benchmarl?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads)](https://pepy.tech/project/benchmarl)
 [![Discord Shield](https://dcbadge.vercel.app/api/server/jEEWCn6T3p?style=flat)](https://discord.gg/jEEWCn6T3p)
@@ -113,6 +113,10 @@ pip install vmas
 pip install "pettingzoo[all]"
 ```
 
+##### MeltingPot
+```bash
+pip install dm-meltingpot
+```
 ##### SMACv2
 
 Follow the instructions on the environment [repository](https://github.com/oxwhirl/smacv2).
@@ -236,12 +240,14 @@ 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) | [18](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        |
+| Environment                                                        | Tasks                                | Cooperation               | Global state | Reward function               | Action space          |    Vectorized    |
+|--------------------------------------------------------------------|--------------------------------------|---------------------------|--------------|-------------------------------|-----------------------|:----------------:|
+| [VMAS](https://github.com/proroklab/VectorizedMultiAgentSimulator) | [18](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        |
+
 
 > [!NOTE]  
 > BenchMARL uses the [TorchRL MARL API](https://github.com/pytorch/rl/issues/1463) for grouping agents.

benchmarl/algorithms/common.py

@@ -7,7 +7,7 @@
 import pathlib
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Any, Dict, Iterable, Optional, Tuple, Type
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 from tensordict import TensorDictBase
 from tensordict.nn import TensorDictModule, TensorDictSequential
@@ -19,6 +19,7 @@
     TensorDictReplayBuffer,
 )
 from torchrl.data.replay_buffers import RandomSampler, SamplerWithoutReplacement
+from torchrl.envs import Compose, Transform
 from torchrl.objectives import LossModule
 from torchrl.objectives.utils import HardUpdate, SoftUpdate, TargetNetUpdater
 
@@ -132,15 +133,15 @@ def get_loss_and_updater(self, group: str) -> Tuple[LossModule, TargetNetUpdater
         return self._losses_and_updaters[group]
 
     def get_replay_buffer(
-        self,
-        group: str,
+        self, group: str, transforms: List[Transform] = None
     ) -> ReplayBuffer:
         """
         Get the ReplayBuffer for a specific group.
         This function will check ``self.on_policy`` and create the buffer accordingly
 
         Args:
             group (str): agent group of the loss and updater
+            transforms (optional, list of Transform): Transforms to apply to the replay buffer ``.sample()`` call
 
         Returns: ReplayBuffer the group
         """
@@ -154,6 +155,7 @@ def get_replay_buffer(
             sampler=sampler,
             batch_size=sampling_size,
             priority_key=(group, "td_error"),
+            transform=Compose(*transforms) if transforms is not None else None,
         )
 
     def get_policy_for_loss(self, group: str) -> TensorDictModule:

benchmarl/conf/experiment/base_experiment.yaml

@@ -82,7 +82,7 @@ evaluation_episodes: 10
 evaluation_deterministic_actions: True
 
 # List of loggers to use, options are: wandb, csv, tensorboard, mflow
-loggers: [wandb]
+loggers: []
 # Create a json folder as part of the output in the format of marl-eval
 create_json: True
 

benchmarl/environments/__init__.py

@@ -5,14 +5,15 @@
 #
 
 from .common import Task
+from .meltingpot.common import MeltingPotTask
 from .pettingzoo.common import PettingZooTask
 from .smacv2.common import Smacv2Task
 from .vmas.common import VmasTask
 
 # 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
 task_config_registry = {}
-for env in [VmasTask, Smacv2Task, PettingZooTask]:
+for env in [VmasTask, Smacv2Task, PettingZooTask, MeltingPotTask]:
     env_config_registry = {
         f"{env.env_name()}/{task.name.lower()}": task for task in env
     }
@@ -31,14 +32,14 @@
 from .pettingzoo.simple_tag import TaskConfig as SimpleTagConfig
 from .pettingzoo.simple_world_comm import TaskConfig as SimpleWorldComm
 from .pettingzoo.waterworld import TaskConfig as WaterworldConfig
+
 from .vmas.balance import TaskConfig as BalanceConfig
 from .vmas.dispersion import TaskConfig as DispersionConfig
 from .vmas.dropout import TaskConfig as DropoutConfig
 from .vmas.give_way import TaskConfig as GiveWayConfig
 from .vmas.navigation import TaskConfig as NavigationConfig
 from .vmas.reverse_transport import TaskConfig as ReverseTransportConfig
 from .vmas.sampling import TaskConfig as SamplingConfig
-
 from .vmas.simple_adverasary import TaskConfig as VmasSimpleAdversaryConfig
 from .vmas.simple_crypto import TaskConfig as VmasSimpleCryptoConfig
 from .vmas.simple_push import TaskConfig as VmasSimplePushConfig

benchmarl/environments/common.py

@@ -96,13 +96,13 @@ def get_env_fun(
             num_envs (int): The number of envs that should be in the batch_size of the returned env.
                 In vectorized envs, this can be used to set the number of batched environments.
                 If your environment is not vectorized, you can just ignore this, and it will be
-                wrapped in a torchrl.envs.SerialEnv with num_envs automatically.
+                wrapped in a :class:`torchrl.envs.SerialEnv` with num_envs automatically.
             continuous_actions (bool): Whether your environment should have continuous or discrete actions.
                 If your environment does not support both, ignore this and refer to the supports_x_actions methods.
             seed (optional, int): The seed of your env
             device (str): the device of your env, you can pass this to any torchrl env constructor
 
-        Returns: a function that takes no arguments and returns a torchrl.envs.EnvBase object
+        Returns: a function that takes no arguments and returns a :class:`torchrl.envs.EnvBase` object
 
         """
         raise NotImplementedError
@@ -242,6 +242,28 @@ def get_reward_sum_transform(self, env: EnvBase) -> Transform:
             reset_keys = env.reset_keys
         return RewardSum(reset_keys=reset_keys)
 
+    def get_env_transforms(self, env: EnvBase) -> List[Transform]:
+        """
+        Returns a list of :class:`torchrl.envs.Transform` to be applied to the env.
+
+        Args:
+            env (EnvBase): An environment created via self.get_env_fun
+
+
+        """
+        return []
+
+    def get_replay_buffer_transforms(self, env: EnvBase) -> List[Transform]:
+        """
+        Returns a list of :class:`torchrl.envs.Transform` to be applied to the :class:`torchrl.data.ReplayBuffer`.
+
+        Args:
+            env (EnvBase): An environment created via self.get_env_fun
+
+
+        """
+        return []
+
     @staticmethod
     def render_callback(experiment, env: EnvBase, data: TensorDictBase):
         try: