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Title: 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-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.  more » « less
Award ID(s):
1903781
PAR ID:
10479218
Author(s) / Creator(s):
;
Editor(s):
Alessandro Astolfi
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Transactions on Automatic Control
Volume:
68
Issue:
4
ISSN:
0018-9286
Page Range / eLocation ID:
2383 to 2390
Subject(s) / Keyword(s):
Adaptive optimal control, data-driven control, policy iteration, reinforcement learning, robustness, stochastic control
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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