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|a UAMI
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1 |
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|a Metelli, Alberto Maria,
|e author.
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1 |
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|a Exploiting environment configurability in reinforcement learning /
|c Alberto Maria Metelli.
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| 264 |
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1 |
|a Amsterdam, Netherlands :
|b IOS Press,
|c 20221207.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
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1 |
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|a Frontiers in artificial intelligence and applications ;
|v volume 361
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| 504 |
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|a Includes bibliographical references.
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| 588 |
0 |
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|a Online resource; title from PDF title page (IOS Press, viewed March 20, 2023).
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|a Intro -- Title page -- Abstract -- Contents -- List of Figures -- List of Tables -- List of Algorithms -- List of Symbols and Notation -- Acknowledgments -- Introduction -- What is Reinforcement Learning? -- Why Environment Configurability? -- Original Contributions -- Overview -- Foundations of Sequential Decision-Making -- Introduction -- Markov Decision Processes -- Markov Reward Processes -- Markov Chains -- Performance Indexes -- Value Functions -- Optimality Criteria -- Exact Solution Methods -- Reinforcement Learning Algorithms -- Temporal Difference Methods -- Function Approximation
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| 505 |
8 |
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|a Policy Search -- Modeling Environment Configurability -- Configurable Markov Decision Processes -- Introduction -- Motivations and Examples -- Definition -- Value Functions -- Bellman Equations and Operators -- Taxonomy -- Related Literature -- Solution Concepts for Conf-MDPs -- Cooperative Setting -- Non-Cooperative Setting -- Learning in Cooperative Configurable Markov Decision Processes -- Learning in Finite Cooperative Conf-MDPs -- Introduction -- Relative Advantage Functions -- Performance Improvement Bound -- Safe Policy Model Iteration -- Theoretical Analysis -- Experimental Evaluation
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| 505 |
8 |
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|a Examples of Conf-MDPs -- Learning in Continuous Conf-MDPs -- Introduction -- Solving Parametric Conf-MDPs -- Relative Entropy Model Policy Search -- Theoretical Analysis -- Approximation of the Transition Model -- Experiments -- Applications of Configurable Markov Decision Processes -- Policy Space Identification -- Introduction -- Generalized Likelihood Ratio Test -- Policy Space Identification in a Fixed Env -- Analysis for the Exponential Family -- Policy Space Identification in a Configurable Env -- Connections with Existing Work -- Experimental Results -- Control Frequency Adaptation
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| 505 |
8 |
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|a Introduction -- Persisting Actions in MDPs -- Bounding the Performance Loss -- Persistent Fitted Q-Iteration -- Persistence Selection -- Related Works -- Experimental Evaluation -- Open Questions -- Discussion and Conclusions -- Modeling Environment Configurability -- Learning in Conf-MDPs -- Applications of Conf-MDPs -- Appendices -- Additional Results and Proofs -- Additional Results and Proofs of Chapter 6 -- Additional Results and Proofs of Chapter 7 -- Additional Results and Proofs of Chapter 8 -- Additional Results and Proofs of Chapter 9 -- Exponential Family Policies
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| 505 |
8 |
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|a Gaussian and Boltzmann Linear Policies as Exponential Family distributions -- Fisher Information Matrix -- Subgaussianity Assumption -- Bibliography
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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0 |
|a Reinforcement learning.
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| 650 |
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6 |
|a Apprentissage par renforcement (Intelligence artificielle)
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| 650 |
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7 |
|a Reinforcement learning
|2 fast
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| 710 |
2 |
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|a IOS Press.
|
| 776 |
0 |
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|i Print version:
|a Metelli, A. M.
|t Exploiting Environment Configurability in Reinforcement Learning
|d Amsterdam : IOS Press, Incorporated,c2022
|z 9781643683621
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| 830 |
|
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|a Frontiers in artificial intelligence and applications ;
|v v. 361.
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