Federated Reinforcement Learning with Environment Heterogeneity

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-environment interaction. We stress the constraint of environment heterogeneity, which means n environments corresponding to these n agents have different state transitions. To obtain a value function or a policy function which optimizes the overall performance in all environments, we propose two federated RL algorithms, QAvg and PAvg. We theoretically prove that these algorithms converge to suboptimal solutions, while such sub-optimality depends on how heterogeneous these n environments are. Moreover, we propose a heuristic that achieves personalization by embedding the n environments into n vectors. The personalization heuristic not only improves the training but also allows for better generalization to new environments.