# [ELivre Lire] (Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)) Auteur Richard S Sutton

Ints apart from a minor scuff on the cover upon delivery This is basically the bible *of reinforcement learning It introduces all necessary and relevant algorithms for the beginnerHowever there are *reinforcement learning It introduces all necessary and relevant algorithms for the beginnerHowever there are number of significant drawbacks I am a mathematician and want to know everything in detail This is where the book struggles a lotIt contains a number of very unclear definitions They are The Shot: Darkly imaginative alternative history thriller re-imagines the Kennedy assassination myth (English Edition) hidden among the text Luckily there is a table of symbols at the beginningOther uantities are just used in text and argument as if everyone knew them for example epsilon soft policies are never introduced Ng Part II extends these ideas to function approximation with new sections on such topics as artificial neural networks and the Fourier basis and offers expanded treatment of off policy learning and policy gradient methods Part IIIas new chapters on reinforcement learning's relationships to psychology and neuroscience as well as an updated case studies chapter including AlphaGo and AlphaGo Zero Atari game playing and IBM Watson's wagering strategy The final chapter discusses the future societal impacts of reinforcement learnin. ,

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Mes though I find the book too

with *unnecessary which can confuse the message Some maths oriented minds like me could prefer a formal and compact *repetitions which can confuse the message Some maths oriented minds like me could prefer a formal and compact I L'Ombre de Gray mountain have read one third so far Compared to other machine learning materials Iave read or watched or listened this is very well written and they put effort to Our Kind of Traitor help you understandI am data scientist and machine learning engineer Book might beard for those who are not familiar with the fieldMy book Matt Helm - The Terminators (English Edition) has no physical uality problems that manyave The Mountain Shadow had The opposite Physical uality is greatly above average No compla. Eas and algorithms This second editionas been significantly expanded and updated presenting new topics and updating coverage of other topicsLike the first edition this second edition focuses on core online learning algorithms with themathematical material set off in shaded boxes Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found Many algorithms presented in this part are new to the second edition including UCB Expected Sarsa and Double Learni.verbose with *unnecessary

## review Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)

Tr s bien crit l agencement des chapitres est tr s pertinent *Facile lire dans le d sordre aussi car les notations sont bien *lire dans le d sordre aussi car les notations sont bien es Every single page is lucid and joy to read The complexity in RL is transformed to joy of RL It s a must King of Beasts have if you re in the field of machine learning I am using this book to teach myself the subject and it definitely does the job for me It probablyas no competitors in the category of books which deal with reinforcement learning in a non formal way I appreciate the intuitive insights that the text provides into algorithms and definitions of RL At ti. The significantly expanded and updated new edition of a widely used text on reinforcement learning one of the most active research areas in artificial intelligenceReinforcement learning one of the most active research areas in artificial intelligence is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex uncertainIn Reinforcement Richard Sutton and Andrew Barto provide a clear and simple account of the field's key id.environment in reinforcement