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In this article, the deep deterministic policy gradient (DDPG) method is usedto learn an optimal control policy of a multi-region state-dependent switchedsystem. We observe good performance of this model-free method and explain it ina rigorous mathematical language. The performance of the learning-based methodsis compared with the optimal solution given by vanilla differential dynamicprogramming (DDP) in three customized environments.

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