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arXiv · 2312.04767

Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems

Mi Zhou·Jiazhi Li·Masood Mortazavi·Ning Yan·Chaouki Abdallah
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Abstract

In this article, a \underline{S}tate-dependent \underline{M}ulti-\underline{A}gent \underline{D}eep \underline{D}eterministic \underline{P}olicy \underline{G}radient (\textbf{SMADDPG}) method is proposed in order to learn an optimal control policy for regionally switched systems. We observe good performance of this method and explain it in a rigorous mathematical language using some simplifying assumptions in order to motivate the ideas and to apply them to some canonical examples. Using reinforcement learning, the performance of the switched learning-based multi-agent method is compared with the vanilla DDPG in two customized demonstrative environments with one and two-dimensional state spaces.

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Mi Zhou, Jiazhi Li, Masood Mortazavi, Ning Yan, Chaouki Abdallah. 2023-12-08. Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems. https://arxiv.org/abs/2312.04767

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