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arXiv · 2312.05166

Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator

Samuel Mallick·Filippo Airaldi·Azita Dabiri·Bart De Schutter
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Abstract

This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use of model predictive control (MPC) as a function approximator for the policy and value functions. The current paper is the first work to extend this idea to the multi-agent setting. We propose the use of a distributed MPC scheme as a function approximator, with a structure allowing for distributed learning and deployment. We then show that Q-learning updates can be performed distributively without introducing nonstationarity, by reconstructing a centralized learning update. The effectiveness of the approach is demonstrated on two numerical examples.

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Samuel Mallick, Filippo Airaldi, Azita Dabiri, Bart De Schutter. 2023-12-08. Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator. https://doi.org/10.1016/j.automatica.2024.111803

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