Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems
This article 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-squa...
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Veröffentlicht in: | IEEE transactions on automatic control 2023-04, Vol.68 (4), p.2383-2390 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This article 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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ISSN: | 0018-9286 1558-2523 |
DOI: | 10.1109/TAC.2022.3172250 |