Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances
This study considers a discrete-time, linear state feedback control strategy rooted in Q-learning, one of the Reinforcement Learning (RL) approaches, to address an adaptive Linear Quadratic (LQ) problem under stochastic disturbances. Q-learning optimizes the state-action policy by estimating the Q-f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2025.2470502 |
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