Titlebook: Reinforcement Learning; Optimal Feedback Con Jinna Li,Frank L. Lewis,Jialu Fan Book 2023 The Editor(s) (if applicable) and The Author(s), u

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Industrial Applications of Game Reinforcement Learning,control?of industrial process operation, particularly dual-rate rougher flotation operation, and performance optimization problems for large-scale industrial processes. To earn high economic profit viewed as one of the operational indices, we present two kinds of off-policy RL methods to learn the o

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Off-Policy Game Reinforcement Learning,of multi-agent systems. In contrast to traditional control protocols, which require complete knowledge of agent dynamics, the presented algorithm is a model-free approach, in that it solves the optimal synchronization problem?without knowing any knowledge of the agent dynamics.

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Book 2023rning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems...?..A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control arc

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Control Using Reinforcement Learning, such that the . control problem can be finally solved for linear multi-player systems without the knowledge of system dynamics. Besides, rigorous proofs of algorithm convergence and unbiasedness of solutions are presented. Simulation results demonstrate the effectiveness of the proposed method.

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