Federated Reinforcement Learning at the Edge

TL;DR

This paper proposes a communication-efficient federated reinforcement learning algorithm for edge systems, enabling multiple agents to collaboratively learn value functions with limited communication, supported by theoretical guarantees and practical evaluations.

Contribution

It introduces a novel algorithm that reduces communication in federated reinforcement learning at the edge, with theoretical analysis and practical implementation.

Findings

The algorithm achieves significant communication reduction.

Theoretical guarantees ensure convergence.

Numerical evaluations demonstrate effectiveness.

Abstract

Modern cyber-physical architectures use data collected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments. However, an important challenge arises as communication exchanges at the edge of networked systems are costly due to limited resources. This paper considers a setup where multiple agents need to communicate efficiently in order to jointly solve a reinforcement learning problem over time-series data collected in a distributed manner. This is posed as learning an approximate value function over a communication network. An algorithm for achieving communication efficiency is proposed, supported with theoretical guarantees, practical implementations, and numerical evaluations. The approach is based on the idea of communicating only when sufficiently informative data is collected.