pip install -r requirements.txt

python main.py --alg cons --env_name ma_gym:PGM-6ag-v0 --max_episodes 80000

python main.py --alg cons --env_name ma_gym:PGM-3ag-v0 --max_episodes 100000

python main.py --alg cons --env_name ma_gym:FindTreasure-v0 --max_episodes 50000 --reuse_network True --individual_rewards False --c_ep 30000 --c_w 0.4

python main_cleanup.py --max_episodes 80000 --c_w 0.4

python main.py --alg cons --env_name ma_gym:PGM-6ag-v0 --pkl_dir model/PGM-6ag/cons/ --load_model --evaluate --render

python main.py --alg cons --env_name ma_gym:PGM-3ag-v0 --pkl_dir model/PGM-3ag/cons/ --load_model --evaluate --render

python main.py --alg cons --env_name ma_gym:FindTreasure-v0 --reuse_network True --individual_rewards False --pkl_dir model/FindTreasure/cons/ --load_model --evaluate --render

python main_cleanup.py --pkl_dir model/Cleanup/cons/ --load_model --evaluate --render

@inproceedings{ba2024cautiously,
  title={Cautiously-Optimistic Knowledge Sharing for Cooperative Multi-Agent Reinforcement Learning},
  author={Ba, Yanwen and Liu, Xuan and Chen, Xinning and Wang, Hao and Xu, Yang and Li, Kenli and Zhang, Shigeng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={16},
  pages={17299--17307},
  year={2024}
}