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標(biāo)題: Titlebook: Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games; Bosen Lian,Wenqian Xue,Bahare Kiumarsi Book 2024 The Ed [打印本頁]

作者: 變成小松鼠    時間: 2025-3-21 17:06
書目名稱Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games影響因子(影響力)




書目名稱Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games影響因子(影響力)學(xué)科排名




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書目名稱Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games被引頻次




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書目名稱Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games讀者反饋學(xué)科排名





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Bosen Lian,Wenqian Xue,Frank L. Lewis,Hamidreza Modares,Bahare Kiumarsi
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Bosen Lian,Wenqian Xue,Frank L. Lewis,Hamidreza Modares,Bahare Kiumarsi
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Bosen Lian,Wenqian Xue,Frank L. Lewis,Hamidreza Modares,Bahare Kiumarsi
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Bosen Lian,Wenqian Xue,Frank L. Lewis,Hamidreza Modares,Bahare Kiumarsi
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Book 2024ination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains – aircraft, robotics, power systems, and communication networks among them – with theoretical insights valuable in tackling the real-world challeng
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Introduction,Natural systems exhibit a remarkable ability to maximize efficiency or minimize costs, embodying an inherent sense of optimality.
作者: CHOKE    時間: 2025-3-24 19:19
Background on Integral and Inverse Reinforcement Learning for Feedback Control,In this chapter, we briefly revisit concepts in optimal control of linear quadratic regulators (LQRs), techniques of integral reinforcement learning (IRL) for LQR, and inverse optimal control theories.
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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
作者: 油膏    時間: 2025-3-25 12:15

作者: keloid    時間: 2025-3-25 17:42
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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Harmony Search Algorithm with Various Evolutionary Elements for Fuzzy Aggregate Production Planningimental results, it can be concluded that each algorithm is suitable for different types of situations. However, for all situations VHSA and NGHSA can obtain good solutions. Furthermore, the proposed VHSA is more effective than other approaches in terms of superiority of solution and required CPU time.
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Pragya Sharma,Nirmala Sharma,Harish Sharmaasmosis. Our goal, in this chapter, is not to give a complete list of the kits and techniques available and employed, what would be quite impossible (a complete book would not be enough to cite all the references related to this topic), but to make an overview of these methods, focusing on their adv
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Fluorescence In Situ Hybridization Probe Validation for Clinical Use,individual validation process prior to clinical use as their commercial counterparts. Validation of a FISH probe involves initial validation and ongoing verification of the test system. Initial validation includes assessment of a probe’s technical specifications, establishment of its standard operat
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Visual perception of motionis book. On the other hand it is intended as a short tutorial introduction into some basic psychological results on motion perception. Motion perception has been a topic of psychological research since more than 100 years. Just within the last thirty years the emerging field of machine vision has be
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