000 02198nam a2200229Ia 4500
003 NULRC
005 20250520103029.0
008 250520s9999 xx 000 0 und d
020 _a9781838644147
040 _cNULRC
050 _aQA 76.73.P98 .B55 2020
100 _aBilgin, Enes
_eauthor
245 0 _aMastering reinforcement learning with python :
_bbuild next-generation, self-learning models using reinforcement learning techniques and best practices /
_cEnes Bilgin
260 _aBirmingham, UK :
_bPackt Publishing, Limited,
_cc2020
300 _axvi, 520 pages :
_billustrations ;
_c24 cm.
365 _bUSD47
504 _aIncludes bibliographical references and index.
505 _aIntroduction to Reinforcement Learning -- Multi-armed Bandits -- Contextual Bandits -- Makings of the Markov Decision Process -- Solving the Reinforcement Learning Problem -- Deep Q-Learning at Scale -- Policy Based Methods -- Model-Based Methods -- Multi-Agent Reinforcement Learning -- Machine Teaching -- Generalization and Domain Randomization -- Meta-reinforcement learning -- Other Advanced Topics -- Autonomous Systems -- Supply Chain Management -- Marketing, Personalization and Finance -- Smart City and Cybersecurity -- Challenges and Future Directions in Reinforcement Learning.
520 _aGet hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key Features Understand how large-scale state-of-the-art RL algorithms and approaches work Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more Explore tips and best practices from experts that will enable you to overcome real-world RL challenges Book Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.
650 _aREINFORCEMENT LEARNING
942 _2lcc
_cBK
999 _c21789
_d21789