Multi-Actor-Attention-Critic

Code for Actor-Attention-Critic for Multi-Agent Reinforcement Learning (Iqbal and Sha, ICML 2019)

Requirements

• Python 3.6.1 (Minimum)
• OpenAI baselines, commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7
• My fork of Multi-agent Particle Environments
• PyTorch, version: 0.3.0.post4
• OpenAI Gym, version: 0.9.4
• Tensorboard, version: 0.4.0rc3 and Tensorboard-Pytorch, version: 1.0 (for logging)

The versions are just what I used and not necessarily strict requirements.

How to Run

All training code is contained within main.py. To view options simply run:

python main.py --help

The “Cooperative Treasure Collection” environment from our paper is referred to as fullobs_collect_treasure in this repo, and “Rover-Tower” is referred to as multi_speaker_listener.

In order to match our experiments, the maximum episode length should be set to 100 for Cooperative Treasure Collection and 25 for Rover-Tower.

Citing our work

If you use this repo in your work, please consider citing the corresponding paper:

@InProceedings{pmlr-v97-iqbal19a,
  title =    {Actor-Attention-Critic for Multi-Agent Reinforcement Learning},
  author =   {Iqbal, Shariq and Sha, Fei},
  booktitle =    {Proceedings of the 36th International Conference on Machine Learning},
  pages =    {2961--2970},
  year =     {2019},
  editor =   {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume =   {97},
  series =   {Proceedings of Machine Learning Research},
  address =      {Long Beach, California, USA},
  month =    {09--15 Jun},
  publisher =    {PMLR},
  pdf =      {http://proceedings.mlr.press/v97/iqbal19a/iqbal19a.pdf},
  url =      {http://proceedings.mlr.press/v97/iqbal19a.html},
}