DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning (IJCAI 2023)
Original PyTorch implementation of DPMAC from
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning by
Canzhe Zhao*, Yanjie Ze*, Jing Dong, Baoxiang Wang, Shuai Li
- Run each .sh file to get the results (remember to substitute the GPU indexes, plz see scripts/train_mpe_nohup.sh and scripts/train_pp_nohup.sh for detailed meanings of each parameter).
- Environment name mapping:
- PP (in codes) -> Predator Prey (in paper).
- Poreference (in codes) -> Cooperative Communication & Navigation (in paper).
- SimSpread (in codes) -> Cooperative Navigation (in paper).
- Dependences: forMARL.yaml.