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Code for "SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning" AAAI 2020

License: Apache License 2.0

Dockerfile 2.22% Shell 2.40% Python 95.37%

pytorch multi-agent-reinforcement-learning smix reinforcement-learning starcraft aaai

smix's Introduction

SMIX(λ)

PyTorch implementation of "SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning". If you use our code please cite the paper.

Installation

SMIX(λ) is tested on SMAC environment. The installation procedure is the same as pymarl.

Run SMIX(λ)

Run SMIX(λ) on 3s5z map:

python3 src/main.py --config=smix --env-config=sc2 with env_args.map_name=3s5z

Results

Replays of SMIX(λ)

Videos of SMIX(λ)'s performance on 3s5z scenario are available respectively at:

Citing SMIX(λ)

In BibTeX format:

@inproceedings{wen20smix,
  title={SMIX($\lambda$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning},
  author={Wen, Chao and Yao, Xinghu and Wang, Yuhui and Tan, Xiaoyang},
  booktitle={Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence},
  year={2020},
}

smix's People

Contributors

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kabibi sx1616039 ssinghzar olgaceban jiaoalvin

smix's Issues

Why there is only one agent in the controller?

Dear chaovven, In https://github.com/chaovven/SMIX/blob/master/src/controllers/basic_controller.py#L75, I found there is only one controller defined here, for homogenous envs, I think it is ok to do so, it is homogenous agents and agents can learn one agent, however, for heterogeneous envs for example 3s5z_vs_3s6z, I think it is not ok to do so since there are ss and zs in each group, right? So, I think there should be defined new agent for each type agent.

How can I calculate the winrate?

Hi, in your paper, I fund there is a winrate metric to evaluate the performance of algorithms. How can I manually calculate the value in StarCraft environment?

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