Federated Reinforcement Learning with Environment Heterogeneity

Hao Jin, Yang Peng, Wenhao Yang +2

We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-e…