EPyMARL is an extension of PyMARL, and includes
- Additional algorithms (IA2C, IPPO, MADDPG, MAA2C and MAPPO)
- Support for Gym environments (on top of the existing SMAC support)
- Option for no-parameter sharing between agents (original PyMARL only allowed for parameter sharing)
- Flexibility with extra implementation details (e.g. hard/soft updates, reward standarization, and more)
- Consistency of implementations between different algorithms (fair comparisons)
See our blog post here: https://agents.inf.ed.ac.uk/blog/epymarl/
We have released our Pareto Actor-Critic algorithm, accepted in TMLR, as part of the E-PyMARL source code.
Find the paper here: https://arxiv.org/abs/2209.14344
Pareto-AC (Pareto-AC), is an actor-critic algorithm that utilises a simple principle of no-conflict games (and, in turn, cooperative games with identical rewards): each agent can assume the others will choose actions that will lead to a Pareto-optimal equilibrium. Pareto-AC works especially well in environments with multiple suboptimal equilibria (a problem is also known as relative over-generalisation). We have seen impressive results in a diverse set of multi-agent games with suboptimal equilibria, including the matrix games of the MARL benchmark, but also LBF variations with high penalties.
To run Pareto-AC in an environment, for example the Penalty game, you can run:
python main.py --config=pac_ns --env-config=gymma with env_args.time_limit=1 env_args.key=matrixgames:penalty-100-nostate-v0
- Extended Python MARL framework - EPyMARL
- Table of Contents
- Installation & Run instructions
- Run an experiment on a Gym environment
- Run a hyperparameter search
- Saving and loading learnt models
- Citing PyMARL and EPyMARL
- License
For information on installing and using this codebase with SMAC, we suggest visiting and reading the original PyMARL README. Here, we maintain information on using the extra features EPyMARL offers.
To install the codebase, clone this repo and install the requirements.txt.
In Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks we introduce and benchmark algorithms in Level-Based Foraging, Multi-Robot Warehouse and Multi-agent Particle environments. To install these please visit:
- Level Based Foraging or install with
pip install lbforaging - Multi-Robot Warehouse or install with
pip install rware - Our fork of MPE, clone it and install it with
pip install -e .
Example of using LBF:
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="lbforaging:Foraging-8x8-2p-3f-v1"Example of using RWARE:
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=500 env_args.key="rware:rware-tiny-2ag-v1"For MPE, our fork is needed. Essentially all it does (other than fixing some gym compatibility issues) is i) registering the environments with the gym interface when imported as a package and ii) correctly seeding the environments iii) makes the action space compatible with Gym (I think MPE originally does a weird one-hot encoding of the actions).
The environments names in MPE are:
...
"multi_speaker_listener": "MultiSpeakerListener-v0",
"simple_adversary": "SimpleAdversary-v0",
"simple_crypto": "SimpleCrypto-v0",
"simple_push": "SimplePush-v0",
"simple_reference": "SimpleReference-v0",
"simple_speaker_listener": "SimpleSpeakerListener-v0",
"simple_spread": "SimpleSpread-v0",
"simple_tag": "SimpleTag-v0",
"simple_world_comm": "SimpleWorldComm-v0",
...
Therefore, after installing them you can run it using:
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="mpe:SimpleSpeakerListener-v0"The pretrained agents are included in this repo here. You can use them with:
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="mpe:SimpleAdversary-v0" env_args.pretrained_wrapper="PretrainedAdversary"and
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="mpe:SimpleTag-v0" env_args.pretrained_wrapper="PretrainedTag"MARBLER is a gym built for the Robotarium to enable free and effortless Sim2Real evaluation of algorithms. Clone it and follow the instructions on its Github to install it.
Example of using MARBLER:
python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=10000 env_args.key="robotarium_gym:PredatorCapturePrey-v0"EPyMARL supports environments that have been registered with Gym. The only difference with the Gym framework would be that the returned rewards should be a tuple (one reward for each agent). In this cooperative framework we sum these rewards together.
Environments that are supported out of the box are the ones that are registered in Gym automatically. Examples are: Level-Based Foraging and RWARE.
To register a custom environment with Gym, use the template below (taken from Level-Based Foraging).
from gym.envs.registration import registry, register, make, spec
register(
id="Foraging-8x8-2p-3f-v1", # Environment ID.
entry_point="lbforaging.foraging:ForagingEnv", # The entry point for the environment class
kwargs={
... # Arguments that go to ForagingEnv's __init__ function.
},
)python3 src/main.py --config=qmix --env-config=gymma with env_args.time_limit=50 env_args.key="lbforaging:Foraging-8x8-2p-3f-v1"In the above command --env-config=gymma (in constrast to sc2 will use a Gym compatible wrapper). env_args.time_limit=50 sets the maximum episode length to 50 and env_args.key="..." provides the Gym's environment ID. In the ID, the lbforaging: part is the module name (i.e. import lbforaging will run automatically).
The config files act as defaults for an algorithm or environment.
They are all located in src/config.
--config refers to the config files in src/config/algs
--env-config refers to the config files in src/config/envs
All results will be stored in the Results folder.
We include a script named search.py which reads a search configuration file (e.g. the included search.config.example.yaml) and runs a hyperparameter search in one or more tasks. The script can be run using
python search.py run --config=search.config.example.yaml --seeds 5 locallyIn a cluster environment where one run should go to a single process, it can also be called in a batch script like:
python search.py run --config=search.config.example.yaml --seeds 5 single 1where the 1 is an index to the particular hyperparameter configuration and can take values from 1 to the number of different combinations.
You can save the learnt models to disk by setting save_model = True, which is set to False by default. The frequency of saving models can be adjusted using save_model_interval configuration. Models will be saved in the result directory, under the folder called models. The directory corresponding each run will contain models saved throughout the experiment, each within a folder corresponding to the number of timesteps passed since starting the learning process.
Learnt models can be loaded using the checkpoint_path and load_step parameters. checkpoint_path should point to a directory stored for a run by epymarl as stated above. The pointed-to directory should contain sub-directories for various timesteps at which checkpoints were stored. If load_step is not provided (by default load_step=0) then the last checkpoint of the pointed-to run is loaded. Otherwise the checkpoint of the closest timestep to load_step will be loaded. After loading, the learning will proceed from the corresponding timestep.
To only evaluate loaded models without any training, set the checkpoint_path and load_step parameters accordingly for the loading, and additionally set evaluate=True. Then, the loaded checkpoint will be evaluated for test_nepisode episodes before terminating the run.
The Extended PyMARL (EPyMARL) codebase was used in Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks.
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, & Stefano V. Albrecht. Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS), 2021
In BibTeX format:
@inproceedings{papoudakis2021benchmarking,
title={Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks},
author={Georgios Papoudakis and Filippos Christianos and Lukas Schäfer and Stefano V. Albrecht},
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS)},
year={2021},
url = {http://arxiv.org/abs/2006.07869},
openreview = {https://openreview.net/forum?id=cIrPX-Sn5n},
code = {https://github.com/uoe-agents/epymarl},
}If you use the original PyMARL in your research, please cite the SMAC paper.
M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T.G.J. Rudner, C.-M. Hung, P.H.S. Torr, J. Foerster, S. Whiteson. The StarCraft Multi-Agent Challenge, CoRR abs/1902.04043, 2019.
In BibTeX format:
@article{samvelyan19smac,
title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},
author = {Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon Whiteson},
journal = {CoRR},
volume = {abs/1902.04043},
year = {2019},
}All the source code that has been taken from the PyMARL repository was licensed (and remains so) under the Apache License v2.0 (included in LICENSE file).
Any new code is also licensed under the Apache License v2.0
