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Handbook of Reinforcement Learning and Control最新文献

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Adaptive Dynamic Programming in the Hamiltonian-Driven Framework 哈密顿驱动框架中的自适应动态规划
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_7
Yongliang Yang, D. Wunsch, Yixin Yin
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引用次数: 1
Dissipativity-Based Verification for Autonomous Systems in Adversarial Environments 对抗环境中基于耗散的自治系统验证
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_10
Aris Kanellopoulos, K. Vamvoudakis, V. Gupta, P. Antsaklis
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引用次数: 0
Bounded Rationality in Differential Games: A Reinforcement Learning-Based Approach 微分博弈中的有限理性:基于强化学习的方法
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_16
Nick-Marios T. Kokolakis, Aris Kanellopoulos, K. Vamvoudakis
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引用次数: 4
Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning 交易效用与不确定性:应用信息价值解决强化学习中的探索-利用困境
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_19
I. Sledge, J. Príncipe
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引用次数: 0
Bounded Rationality in Learning, Perception, Decision-Making, and Stochastic Games 学习、感知、决策和随机博弈中的有限理性
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_17
P. Tsiotras
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引用次数: 1
What May Lie Ahead in Reinforcement Learning 强化学习的未来是什么
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_1
D. Cansever
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引用次数: 2
Fundamental Design Principles for Reinforcement Learning Algorithms 强化学习算法的基本设计原则
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_4
Adithya M. Devraj, A. Bušić, Sean P. Meyn
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引用次数: 14
Decision-Making for Complex Systems Subjected to Uncertainties—A Probability Density Function Control Approach 具有不确定性的复杂系统决策——一种概率密度函数控制方法
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_23
Aiping Wang, Hong Wang
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引用次数: 2
Reinforcement Learning: An Industrial Perspective 强化学习:工业视角
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_21
A. Surana
{"title":"Reinforcement Learning: An Industrial Perspective","authors":"A. Surana","doi":"10.1007/978-3-030-60990-0_21","DOIUrl":"https://doi.org/10.1007/978-3-030-60990-0_21","url":null,"abstract":"","PeriodicalId":183321,"journal":{"name":"Handbook of Reinforcement Learning and Control","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127001324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Mixed Density Methods for Approximate Dynamic Programming 近似动态规划的混合密度方法
Handbook of Reinforcement Learning and Control Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-60990-0_5
Max L. Greene, Patryk Deptula, R. Kamalapurkar, W. Dixon
{"title":"Mixed Density Methods for Approximate Dynamic Programming","authors":"Max L. Greene, Patryk Deptula, R. Kamalapurkar, W. Dixon","doi":"10.1007/978-3-030-60990-0_5","DOIUrl":"https://doi.org/10.1007/978-3-030-60990-0_5","url":null,"abstract":"","PeriodicalId":183321,"journal":{"name":"Handbook of Reinforcement Learning and Control","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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