Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common app...
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| Format: | Online |
| Sprache: | Englisch |
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The MIT Press
2023
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| Online-Zugang: | ONIX_20230731_9780262374026_15 |
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| _version_ | 1869526191409463296 |
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| author | Bellemare, Marc G. Dabney, Will Rowland, Mark |
| author_browse | Bellemare, Marc G. Dabney, Will Rowland, Mark |
| author_facet | Bellemare, Marc G. Dabney, Will Rowland, Mark |
| author_sort | Bellemare, Marc G. |
| collection | Directory of Open Access Books |
| description | The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions. |
| format | Online |
| id | doab-20.500.12854ir-111581 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1115812024-04-04T14:41:15Z Distributional Reinforcement Learning Bellemare, Marc G. Dabney, Will Rowland, Mark Computer Science/Machine Learning & Neural Networks thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions. 2023-07-31T10:53:52Z 2023-07-31T10:53:52Z 2023 book ONIX_20230731_9780262374026_15 9780262374026 9780262048019 https://directory.doabooks.org/handle/20.500.12854/111581 eng Adaptive Computation and Machine Learning series image/jpeg n/a https://doi.org/10.7551/mitpress/14207.001.0001 The MIT Press The MIT Press 10.7551/mitpress/14207.001.0001 10.7551/mitpress/14207.001.0001 ae0cf962-f685-4933-93d1-916defa5123d 9780262374026 9780262048019 The MIT Press 384 Cambridge open access |
| spellingShingle | Computer Science/Machine Learning & Neural Networks thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Bellemare, Marc G. Dabney, Will Rowland, Mark Distributional Reinforcement Learning |
| title | Distributional Reinforcement Learning |
| title_full | Distributional Reinforcement Learning |
| title_fullStr | Distributional Reinforcement Learning |
| title_full_unstemmed | Distributional Reinforcement Learning |
| title_short | Distributional Reinforcement Learning |
| title_sort | distributional reinforcement learning |
| topic | Computer Science/Machine Learning & Neural Networks thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Computer Science/Machine Learning & Neural Networks thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20230731_9780262374026_15 |
| work_keys_str_mv | AT bellemaremarcg distributionalreinforcementlearning AT dabneywill distributionalreinforcementlearning AT rowlandmark distributionalreinforcementlearning |