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15 January 2025 Research on optimization of intelligent control systems based on deep reinforcement learning
Tao Huang
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Proceedings Volume 13513, The International Conference Optoelectronic Information and Optical Engineering (OIOE2024); 1351343 (2025) https://doi.org/10.1117/12.3056634
Event: The International Conference Optoelectronic Information and Optical Engineering (OIOE2024), 2024, Wuhan, China
Abstract
This study constructs an intelligent control system based on Deep Q-Network (DQN) to improve control efficiency in complex dynamic environments. By employing methods such as input state preprocessing, control action design, and reward function optimization, the system achieves rapid convergence and high-precision control. Through multiple simulation experiments, the results show that the proposed DQN model outperforms the traditional Q-learning algorithm in terms of average cumulative rewards, control accuracy, and energy consumption, demonstrating significant performance advantages. The study indicates that the system possesses good adaptability and efficiency in intelligent control applications, providing important groundwork for future research.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tao Huang "Research on optimization of intelligent control systems based on deep reinforcement learning", Proc. SPIE 13513, The International Conference Optoelectronic Information and Optical Engineering (OIOE2024), 1351343 (15 January 2025); https://doi.org/10.1117/12.3056634
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KEYWORDS
Control systems
Education and training
Design
Intelligence systems
Detection and tracking algorithms
Data modeling
Control systems design
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