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|a Foundations of deep reinforcement learning
|b theory and practice in Python
|c Laura Graesser, Wah Loon Keng
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|c [2020?]
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|a "Foundations of Deep Reinforcement Learning is the fastest, most accessible introduction to deep reinforcement learning (DRL). Deep reinforcement learning (DRL) systems are achieving breakthrough performance in many applications. DRL machine learning resembles human learning in multiple ways: It's exceptionally flexible, can learn and discover strategies without supervision, and can efficiently balance exploration with exploitation. The authors begin with intuition, then carefully explain DRL theory and algorithms, and finish with implementations and practical tips. Throughout, the authors bridge theory and practice, helping you integrate components and tune parameters that must work together optimally for DRL algorithms to succeed. They illuminate important recent advances through hands-on examples written in Python and built with their advanced SLM Lab software library. This guide is intended for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python"--
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"Foundations of Deep Reinforcement Learning is the fastest, most accessible introduction to deep reinforcement learning (DRL). Deep reinforcement learning (DRL) systems are achieving breakthrough performance in many applications. DRL machine learning resembles human learning in multiple ways: It's exceptionally flexible, can learn and discover strategies without supervision, and can efficiently balance exploration with exploitation. The authors begin with intuition, then carefully explain DRL theory and algorithms, and finish with implementations and practical tips. Throughout, the authors bridge theory and practice, helping you integrate components and tune parameters that must work together optimally for DRL algorithms to succeed. They illuminate important recent advances through hands-on examples written in Python and built with their advanced SLM Lab software library. This guide is intended for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python"-- |
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xxiv, 379 pages; illustrations; 24 cm |
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Graesser, Laura author., Foundations of deep reinforcement learning theory and practice in Python Laura Graesser, Wah Loon Keng, Boston Addison-Wesley [2020?], ©2020, xxiv, 379 pages : illustrations ; 24 cm., text txt rdacontent, unmediated n rdamedia, volume nc rdacarrier, Addison-Wesley data & analytics series, Includes bibliographical references and index., "Foundations of Deep Reinforcement Learning is the fastest, most accessible introduction to deep reinforcement learning (DRL). Deep reinforcement learning (DRL) systems are achieving breakthrough performance in many applications. DRL machine learning resembles human learning in multiple ways: It's exceptionally flexible, can learn and discover strategies without supervision, and can efficiently balance exploration with exploitation. The authors begin with intuition, then carefully explain DRL theory and algorithms, and finish with implementations and practical tips. Throughout, the authors bridge theory and practice, helping you integrate components and tune parameters that must work together optimally for DRL algorithms to succeed. They illuminate important recent advances through hands-on examples written in Python and built with their advanced SLM Lab software library. This guide is intended for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python"-- Provided by publisher., Current Copyright Fee: GBP25.00 0 Uk, Reinforcement learning., Python (Computer program language), Keng, Wah Loon author., Addison-Wesley data and analytics series., British Library DSC m20/.10147, British Library HMNTS YKL.2021.a.4630 |
| spellingShingle |
Graesser, Laura, Keng, Wah Loon, Foundations of deep reinforcement learning: theory and practice in Python, Addison-Wesley data and analytics series, "Foundations of Deep Reinforcement Learning is the fastest, most accessible introduction to deep reinforcement learning (DRL). Deep reinforcement learning (DRL) systems are achieving breakthrough performance in many applications. DRL machine learning resembles human learning in multiple ways: It's exceptionally flexible, can learn and discover strategies without supervision, and can efficiently balance exploration with exploitation. The authors begin with intuition, then carefully explain DRL theory and algorithms, and finish with implementations and practical tips. Throughout, the authors bridge theory and practice, helping you integrate components and tune parameters that must work together optimally for DRL algorithms to succeed. They illuminate important recent advances through hands-on examples written in Python and built with their advanced SLM Lab software library. This guide is intended for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python"--, Reinforcement learning., Python (Computer program language) |
| title |
Foundations of deep reinforcement learning: theory and practice in Python |
| title_auth |
Foundations of deep reinforcement learning theory and practice in Python |
| title_full |
Foundations of deep reinforcement learning theory and practice in Python Laura Graesser, Wah Loon Keng |
| title_fullStr |
Foundations of deep reinforcement learning theory and practice in Python Laura Graesser, Wah Loon Keng |
| title_full_unstemmed |
Foundations of deep reinforcement learning theory and practice in Python Laura Graesser, Wah Loon Keng |
| title_in_hierarchy |
Foundations of deep reinforcement learning: theory and practice in Python ([2020?]) |
| title_short |
Foundations of deep reinforcement learning |
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foundations of deep reinforcement learning theory and practice in python |
| title_sub |
theory and practice in Python |
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Reinforcement learning., Python (Computer program language) |
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Reinforcement learning., Python (Computer program language) |