Reinforcement Learning An Introduction Adaptive Computation and Machine Learning series Richard S Sutton Andrew G Barto 9780262039246 Books Livres gratuit EFK

Reinforcement Learning An Introduction Adaptive Computation and Machine Learning series Richard S Sutton Andrew G Barto 9780262039246 Books lis Reinforcement%20Learning%20An%20Introduction%20Adaptive%20Computation%20and%20Machine%20Learning%20series%20Richard%20S%20Sutton%20Andrew%20G%20Barto%209780262039246%20Books

YUI

Reinforcement%20Learning%20An%20Introduction%20Adaptive%20Computation%20and%20Machine%20Learning%20series%20Richard%20S%20Sutton%20Andrew%20G%20Barto%209780262039246%20Books

lis Reinforcement Learning An Introduction Adaptive Computation and Machine Learning series Richard S Sutton Andrew G Barto 9780262039246 Books YUI

A History of the Cinema from Its Origins to 1970 Eric Rhode 9780140220285 Books Lire en ligne CNH

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement 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, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical 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 Learning. 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 III has 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 learning.

Richard S. Sutton, Andrew G. Barto,Reinforcement Learning An Introduction (Adaptive Computation and Machine Learning series),A Bradford Book,0262039249,Reinforcement learning,Reinforcement learning.,COMPUTERS,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Science,Computer Science/Machine Learning Neural Networks,Computer/General,Intelligence (AI) Semantics,Machine Learning Neural Networks,Machine learning,Non-Fiction,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States,reinforcement learning; artificial intelligence; reward; prediction; control; temporal difference learning; Markov decision processes; dynamic programming; online learning; neural networks; deep reinforcement learning; neuro-dynamic programming; approximate dynamic programming; trial and error; bandit algorithms; off-policy learning; function approximation; value functions; Monte Carlo methods; planning,reinforcement learning;artificial intelligence;reward;prediction;control;temporal difference learning;Markov decision processes;dynamic programming;online learning;neural networks;deep reinforcement learning;neuro-dynamic programming;approximate dynamic programming;trial and error;bandit algorithms;off-policy learning;function approximation;value functions;Monte Carlo methods;planning,COMPUTERS / Intelligence (AI) Semantics,Machine learning

Reinforcement Learning An Introduction Adaptive Computation and Machine Learning series Richard S Sutton Andrew G Barto 9780262039246 Books Reviews :

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement 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, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical 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 Learning. 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 III has 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 learning.

Richard S. Sutton, Andrew G. Barto,Reinforcement Learning An Introduction (Adaptive Computation and Machine Learning series),A Bradford Book,0262039249,Reinforcement learning,Reinforcement learning.,COMPUTERS,COMPUTERS / Intelligence (AI) Semantics,Computer Applications,Computer Science,Computer Science/Machine Learning Neural Networks,Computer/General,Intelligence (AI) Semantics,Machine Learning Neural Networks,Machine learning,Non-Fiction,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States,reinforcement learning; artificial intelligence; reward; prediction; control; temporal difference learning; Markov decision processes; dynamic programming; online learning; neural networks; deep reinforcement learning; neuro-dynamic programming; approximate dynamic programming; trial and error; bandit algorithms; off-policy learning; function approximation; value functions; Monte Carlo methods; planning,reinforcement learning;artificial intelligence;reward;prediction;control;temporal difference learning;Markov decision processes;dynamic programming;online learning;neural networks;deep reinforcement learning;neuro-dynamic programming;approximate dynamic programming;trial and error;bandit algorithms;off-policy learning;function approximation;value functions;Monte Carlo methods;planning,COMPUTERS / Intelligence (AI) Semantics,Machine learning

Reinforcement Learning An Introduction (Adaptive Computation and Machine Learning series) [Richard S. Sutton, Andrew G. Barto] on . PBThe significantly expanded and updated new edition of a widely used text on reinforcement learning

 

Product details Series Adaptive Computation and Machine Learning series Hardcover 552 pages Publisher A Bradford Book; second edition edition (November 13, 2018) Language English ISBN-10 0262039249

"" [Review ]

Tags : lis,