Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems

Bo Pang; Zhong-Ping Jiang

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arXiv · 2107.07788

Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems

Bo Pang·Zhong-Ping Jiang

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Abstract

This paper studies the adaptive optimal stationary control of continuous-time linear stochastic systems with both additive and multiplicative noises, using reinforcement learning techniques. Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to find iteratively near-optimal policies of the adaptive optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy. The solutions given by the proposed optimistic least-squares-based policy iteration are proved to converge to a small neighborhood of the optimal solution with probability one, under mild conditions. The application of the proposed algorithm to a triple inverted pendulum example validates its feasibility and effectiveness.

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BibTeX RIS

Bo Pang, Zhong-Ping Jiang. 2021-07-16. Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems. https://arxiv.org/abs/2107.07788

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