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      1. 360个人图书馆 - 全球最大的电子图书馆

        多主体强化学习协作策略研究

      2. 特别提示多主体强化学习协作策略研究有80%的机率在90万本电子图书馆中可以在线阅读请您考虑清楚后再决定是否花费点数获得90万本电子图书馆的专用帐号即使没有多主体强化学习协作策略研究在线阅读和?#30053;?#35813;帐号还有90万本其它图书可以在线阅读或高清?#30053;?
      3. 多主体的研究与应用是近年来备受关注的热点领 域多主体强化学习理论与方法多主体协作策略的 研究是该领域重要研究方向其理论和应用价值极为 广泛备受广大从事计算机应用人工智能自动控 制以及经济管理等领域研究者的关注孙若莹赵 刚所著的多主体强化学习协作策略研究清晰地介 绍了多主体强化学习及多主体协作等基本概念和基 础内容明确地阐述了有关多主体强化学习协作策 略研究的发展过程及最新动向深入地?#25945;?#20102;多主体 强化学习与协作策略的理论与方法具体地分析了多 主体强化学习与协作策略在相关研究领域的应用方法
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        目录:
        Chapter1Introduction1.1ReinforcementLearning1.1.1GeneralityofReinforcementLearning1.1.2ReinforcementLearningonMarkovDecisionP
        Chapter 1Introduction1.1Reinforcement Learning1.1.1Generality of Reinforcement Learning1.1.2Reinforcement Learning on Markov Decision Processes1.1.3Integrating Reinforcement Learning into Agent Architecture1.2Multiagent Reinforcement Learning1.2.1Multiagent Systems1.2.2Reinforcement Learning in Multiagent Systems1.2.3Learning and Coordination in Multiagent Systems1.3Ant System for Stochastic Combinatorial Optimization1.3.1Ants Forage Behavior1.3.2Ant Colony Optimization1.3.3MAX-MIN Ant System1.4Motivations and Consequences1.5Book SummaryBibliographyChapter 2Reinforcement Learning and Its Combination with Ant Colony System2.1Introduction2.2Investigation into Reinforcement Learning and Swarm Intelligence2.2.1Temporal Differences Learning Method2.2.2Active Exploration and Experience Replay in Reinforcement Learning2.2.3Ant Colony System for Traveling Salesman Problem2.3The Q-ACS Multiagent Learning Method2.3.1The Q-ACS Learning Algorithm2.3.2Some Properties of the Q-ACS Learning Method2.3.3Relation with Ant-Q Learning Method2.4Simulations and Results2.5ConclusionsBibliographyChapter 3Multiagent Learning Methods Based on Indirect Media Information Sharing3.1Introduction3.2The Multiagent Learning Method Considering Statistics Features3.2.1Accelerated K-certainty Exploration3.2.2The T-ACS Learning Algorithm3.3The Heterogeneous Agents Learning3.3.1The D-ACS Learning Algorithm3.3.2Some Discussions about the D-ACS Learning Algorithm3.4Comparisons with Related State-of-the-arts3.5Simulations and Results3.5.1Experimental Results on Hunter Game3.5.2Experimental Results on Traveling Salesman Problem3.6ConclusionsBibliographyChapter 4Action Conversion Mechanism in Multiagent Reinforcement Learning4.1Introduction4.2Model-Based Reinforcement Learning4.2.1Dyna-Q Architecture4.2.2Prioritized Sweeping Method4.2.3Minimax Search and Reinforcement Learning4.2.4RTP-Q Learning4.3The Q-ac Multiagent Reinforcement Learning4.3.1Task Model4.3.2Converting Action4.3.3Multiagent Cooperation Methods4.3.4Q-value Update4.3.5The Q-ac Learning Algorithm4.3.6Using Adversarial Action Instead o{ ~ Probability Exploration4.4Simulations and Results4.5ConclusionsBibliographyChapter 5Multiagent Learning Approaches Applied to Vehicle Routing Problems5.1Introduction5.2Related State-of-the-arts5.2.1Some Heuristic Algorithms5.2.2The Vehicle Routing Problem with Time Windows5.3The Multiagent Learning Applied to CVRP and VRPTW5.4Simulations and Results5.5ConclusionsBibliographyChapter 6Multiagent learning Methods Applied to Multicast Routing Problems6.1Introduction6.2Multiagent Q-learning Applied to the Network Routing6.2.1Investigation into Q-routing6.2.2AntNet Investigation6.3Some Multicast Routing in Mobile Ad Hoc Networks6.4The Multiagent Q-learning in the Q-MAP Multicast Routing Method6.4.1Overview of t