Titlebook: Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games; Bosen Lian,Wenqian Xue,Bahare Kiumarsi Book 2024 The Ed

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1430-9491 with illustrative examples to bring readers up to speed.Algo.Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games. develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcem

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Inverse Reinforcement Learning for Two-Player Zero-Sum Gamesorks, manufacturing, and industrial systems. In control theory, the objective is to find control inputs that counteract disturbances and stabilize these systems. The framework of zero-sum games (Lewis et al. .) provides a powerful method to achieve this goal.

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Inverse Reinforcement Learning for Optimal Control Systems and Ng .; Chu et al. .; Lin et al. .; Self et al. .; Song et al. .; Syed and Schapire .), where a learner leverages observations of an expert’s behavior to uncover the unknown expert cost functions and replicate the expert’s behavior.

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Integral Reinforcement Learning for Optimal Trackingy. Optimal control theory aims to achieve this goal by determining a control law that not only stabilizes the error dynamics but also minimizes a predefined performance index. Reinforcement learning (RL) algorithms have proven to be effective in solving the . (OTCP) for both discrete-time (Dierks an

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