Deep reinforcement learning hands-on :: apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more /
With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perfor...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing Ltd.,
[2020]
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781838820046 1838820043 |
Internformat
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| 245 | 1 | 0 | |a Deep reinforcement learning hands-on : |b apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / |c Maxim Lapan. |
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| 505 | 0 | |a Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients -- an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions -- PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free -- ImaginationAlphaGo ZeroRL in Discrete OptimisationMulti-agent RL. | |
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| contents | Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients -- an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions -- PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free -- ImaginationAlphaGo ZeroRL in Discrete OptimisationMulti-agent RL. |
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| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:29:49Z |
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| spelling | Lapan, Maxim, author. Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Maxim Lapan. Second edition. Birmingham, UK : Packt Publishing Ltd., [2020] 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from digital title page (viewed on April 09, 2020). Includes bibliographical references and index. Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients -- an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions -- PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free -- ImaginationAlphaGo ZeroRL in Discrete OptimisationMulti-agent RL. With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks. Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage par renforcement (Intelligence artificielle) Apprentissage automatique. Traitement automatique des langues naturelles. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Natural language processing (Computer science) fast Reinforcement learning fast Aprenentatge per reforç. lemac has work: Deep reinforcement learning hands-on (Text) https://id.oclc.org/worldcat/entity/E39PCFMdXM9QdxRRWWrMjdCPcP https://id.oclc.org/worldcat/ontology/hasWork Print version : 9781838826994 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2366458 Volltext |
| spellingShingle | Lapan, Maxim Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Table of ContentsWhat Is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksHigher-Level RL librariesDQN ExtensionsWays to Speed up RLStocks Trading Using RLPolicy Gradients -- an AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticTraining Chatbots with RLThe TextWorld environmentWeb NavigationContinuous Action SpaceRL in RoboticsTrust Regions -- PPO, TRPO, ACKTR, and SACBlack-Box Optimization in RLAdvanced explorationBeyond Model-Free -- ImaginationAlphaGo ZeroRL in Discrete OptimisationMulti-agent RL. Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage par renforcement (Intelligence artificielle) Apprentissage automatique. Traitement automatique des langues naturelles. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Natural language processing (Computer science) fast Reinforcement learning fast Aprenentatge per reforç. lemac |
| subject_GND | http://id.loc.gov/authorities/subjects/sh92000704 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh88002425 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D009323 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / |
| title_auth | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / |
| title_exact_search | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / |
| title_full | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Maxim Lapan. |
| title_fullStr | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Maxim Lapan. |
| title_full_unstemmed | Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / Maxim Lapan. |
| title_short | Deep reinforcement learning hands-on : |
| title_sort | deep reinforcement learning hands on apply modern rl methods to practical problems of chatbots robotics discrete optimization web automation and more |
| title_sub | apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more / |
| topic | Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage par renforcement (Intelligence artificielle) Apprentissage automatique. Traitement automatique des langues naturelles. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Natural language processing (Computer science) fast Reinforcement learning fast Aprenentatge per reforç. lemac |
| topic_facet | Reinforcement learning. Machine learning. Natural language processing (Computer science) Artificial intelligence. Natural Language Processing Artificial Intelligence Machine Learning Apprentissage par renforcement (Intelligence artificielle) Apprentissage automatique. Traitement automatique des langues naturelles. Intelligence artificielle. artificial intelligence. Artificial intelligence Machine learning Reinforcement learning Aprenentatge per reforç. |
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| work_keys_str_mv | AT lapanmaxim deepreinforcementlearninghandsonapplymodernrlmethodstopracticalproblemsofchatbotsroboticsdiscreteoptimizationwebautomationandmore |