Federated Reinforcement Learning at the Edge
This addresses communication efficiency for edge-based systems in distributed reinforcement learning, but it appears incremental as it builds on existing federated learning concepts.
The paper tackles the challenge of costly communication in federated reinforcement learning at the edge by proposing an algorithm that communicates only when sufficiently informative data is collected, supported by theoretical guarantees and numerical evaluations.
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.