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      1. 360個人圖書館 - 全球最大的電子圖書館

        多主體強化學習協作策略研究

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      3.   多主體的研究與應用是近年來備受關注的熱點領 域,多主體強化學習理論與方法、多主體協作策略的 研究是該領域重要研究方向,其理論和應用價值極為 廣泛,備受廣大從事計算機應用、人工智能、自動控 制、以及經濟管理等領域研究者的關注。孫若瑩、趙 剛所著的《多主體強化學習協作策略研究》清晰地介 紹了多主體、強化學習及多主體協作等基本概念和基 礎內容,明確地闡述了有關多主體強化學習、協作策 略研究的發展過程及最新動向,深入地探討了多主體 強化學習與協作策略的理論與方法,具體地分析了多 主體強化學習與協作策略在相關研究領域的應用方法 。
        全書系統脈絡清晰、基本概念清楚、圖表分析直 觀,注重內容的體系化和實用性。通過本書的閱讀和 學習,讀者即可掌握多主體強化學習及協作策略的理 論和方法,更可了解在實際工作中應用這些研究成果 的手段。本書可作為從事計算機應用、人工智能、自 動控制、以及經濟管理等領域研究者的學習和閱讀參 考,同時高等院校相關專業研究生以及人工智能愛好 者也可從中獲得借鑒。
        目錄:
        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