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

Reference

S. Mallick, F. Airaldi, A. Dabiri, and B. De Schutter, "Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator," Automatica, vol. 167, p. 111803, Sept. 2024.

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 a numerical example.

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Bibtex entry

@article{MalAir:24-012,
author={S. Mallick and F. Airaldi and A. Dabiri and B. {D}e Schutter},
title={Multi-Agent Reinforcement Learning via Distributed {MPC} as a Function Approximator},
journal={Automatica},
volume={167},
pages={111803},
month=sep,
year={2024},
doi={10.1016/j.automatica.2024.111803}
}

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