Research Area:  Machine Learning

Abstract:

We study Federated Reinforcement Learning (FedRL) problem in which n agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. In this paper, 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 algorithms, we propose two federated RL algorithms, QAvg and PAvg. We theoretically prove that these algorithms converge to suboptimal solutions, while such suboptimality 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.

Keywords:  
Federated Reinforcement Learning
Environment Heterogeneity
sub optimality
Environment heterogeneity

Author(s) Name:  Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, Zhihua Zhang

Publisher name:  PMLR

DOI:  

Volume Information:   Volume 151

Paper Link:   https://proceedings.mlr.press/v151/jin22a.html