wsg1873 / Multi-Explore

Code for "Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning"

Multi-Explore

Code for Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning (Iqbal and Sha, arXiv 1905.12127)

Requirements

• Python 3.7.3
• OpenAI baselines, commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7
• PyTorch, version: 1.2.0
• OpenAI Gym, version: 0.14.0
• ViZDoom, version: 1.1.7
• Tensorboard, version: 1.14.0 and Tensorboard-Pytorch, version: 1.8 (for logging)

The versions are what were used in this project but are not necessarily strict requirements.

How to Run

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

python main.py --help

All hyperparameters can be found in the Appendix of the paper. Default hyperparameters are for Task 1 in the GridWorld environment using 2 agents.

Citing our work

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

@article{iqbal2019coordinated,
  title={Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning},
  author={Iqbal, Shariq and Sha, Fei},
  journal={arXiv preprint arXiv:1905.12127},
  year={2019}
}