yuanleirl/icq

Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight https://arxiv.org/abs/2106.03400)

Implicit Constraint Q-Learning

This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SMAC, the corresponding paper of ICQ is Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning.

Requirements

Single-agent:

Please enter the ICQ_mu, ICQ_softmax, ICQ-antmaze_mu and ICQ-antmaze_softmax folders.

• python=3.6.5
• Datasets for Deep Data-Driven Reinforcement Learning (D4RL)
• torch=1.1.0

Multi-agent:

Please enter the ICQ-MA folder. Then, set up StarCraft II and SMAC:

bash install_sc2.sh

This will download SC2 into the 3rdparty folder and copy the maps necessary to run over.

The requirements.txt file can be used to install the necessary packages into a virtual environment (not recommended).

Quick Start

Single-agent:

$ python3 main.py

Multi-agent:

$ python3 src/main.py --config=offpg_smac --env-config=sc2 with env_args.map_name=3s_vs_3z

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.

Citing

If you find this open source release useful, please reference in your paper (it is our honor):

@article{yang2021believe,
  title={Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning},
  author={Yang, Yiqin and Ma, Xiaoteng and Li, Chenghao and Zheng, Zewu and Zhang, Qiyuan and Huang, Gao and Yang, Jun and Zhao, Qianchuan},
  journal={arXiv preprint arXiv:2106.03400},
  year={2021}
}

Note

• If you have any questions, please contact me: [email protected].
• The implementation is based on PyMARL, SMAC codebases and DOP which are open-sourced.