ellagic-acid
发表于 2025-3-23 09:47:23
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Externalize
发表于 2025-3-23 14:12:39
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inflate
发表于 2025-3-23 18:33:47
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
想象
发表于 2025-3-24 00:55:27
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安抚
发表于 2025-3-24 02:42:14
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.
Talkative
发表于 2025-3-24 08:03:05
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Rankle
发表于 2025-3-24 11:18:05
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
Inoperable
发表于 2025-3-24 15:57:03
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Phonophobia
发表于 2025-3-24 19:40:11
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.
sulcus
发表于 2025-3-25 00:45:21
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