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

Instructions

1. 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).
2. Environment name mapping:
   1. PP (in codes) -> Predator Prey (in paper).
   2. Poreference (in codes) -> Cooperative Communication & Navigation (in paper).
   3. SimSpread (in codes) -> Cooperative Navigation (in paper).
3. Dependences: forMARL.yaml.