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Efficient Communication in Multi-Agent Distributed Reinforcement Learning

2021-09-03 10:06:53
Daniel Jarne Ornia, Manuel Mazo Jr
arXiv_AI
     
Abstract

We present in this work an approach to reduce the communication of information needed on a multi-agent learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a distributed Q-learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents explore the MDP and communicate experiences to a central learner only when necessary, which performs updates of the actor Q functions. We analyse the convergence guarantees retained with respect to a regular Q-learning algorithm, and present experimental results showing that event-based communication results in a substantial reduction of data transmission rates in such distributed systems. Additionally, we discuss what effects (desired and undesired) these event-based approaches have on the learning processes studied, and how they can be applied to more complex multi-agent learning systems.

Abstract (translated)

URL

https://arxiv.org/abs/2109.01417

PDF

https://arxiv.org/pdf/2109.01417.pdf

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