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

Abstract

In this article, a State-dependent Multi-Agent Deep Deterministic Policy Gradient (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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Reproductions