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| Reinforcement Learning for Mean-field System with Unknown System Information |
| LIN Yingxia1, QI Qingyuan2
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1. College of Automation, Qingdao University, Qingdao 266071, China;
2. Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao 266000, China |
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Abstract In this paper, the infinite horizon linear quadratic (LQ) optimal control problem for mean-field system with unknown system information is solved by using a completely model-free reinforcement learning (RL) approach. Although the introduction of the mean-field terms in system dynamics and the cost function will destroy the adaptiveness of the control law, the optimal stabilization control is successfully obtained based on the proposed RL algorithm and the Least Squares Temporal Difference estimation. In addition, combined with the idea of introducing off-policy learning, the control policy is further improved. We also prove that the algorithm produces stable policies given that the estimation errors remain small.
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Received: 15 May 2023
Published: 09 October 2025
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