Free Reinforcement Learning: An Introduction (Adaptive Computation And Machine Learning Series) Ebooks Online

                
              
Free 
Reinforcement Learning: An Introduction (Adaptive Computation And Machine Learning Series) Ebooks Online
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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
File Size: 8287 KB
Print Length: 344 pages
Publisher: A Bradford Book (February 26, 1998)
Publication Date: February 26, 1998
Sold by:  Digital Services LLC
Language: English
ASIN: B008H5Q8VA
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Best Sellers Rank: #485,062 Paid in Kindle Store (See Top 100 Paid in Kindle Store)   #491 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics   #7793 in Kindle Store > Kindle eBooks > Computers & Technology   #171059 in Kindle Store > Kindle eBooks > Nonfiction
Reinforcement Learning is an exceptionally clear introduction to a field that also goes under names such as approximate dynamic programming, adaptive dynamic programming and neuro-dynamic programming. The book is written entirely from the perspective of computer science, where problems tend to have discrete states (although potentially large state spaces) and (typically) small action spaces.The book provides numerous step-by-step algorithms that makes it relatively easy to get started writing algorithms. The presentation uses minimal mathematics, and avoids the difficult theory supporting the convergence proofs, making it a nice introduction for undergraduates and graduates alike. But throughout the presentation is evidence of extensive experience with applying these methods to a range of classical problems in artificial intelligence.Students interested in a stronger theoretical foundation should look at Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3). My recent book, Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics), puts far more emphasis on mathematical modeling, and presents the field more from the perspective of the operations research community.
I have this book more than a year now and I am going through it for the second time, so I think I have a pretty good picture about it.The book consists of three parts. In the first part, "The Problem", the authors define the scope of issues reinfocement learning is dealing with and they give some interesting introductory examples. Then, they move on to the concept of evaluative feedback and, eventually, define the reinforcement learning problem formally.The second part, "Elementary Solution Methods" consists of three more-less independent subparts: Dynamic Programming, Monte Carlo Methods and Temporal Difference Learning. All three fundamental reinforcement learning methods are presented in an interesting way and using good examples. Personally, I liked the TD-Learning part best and I agree that this method is indeed the central method and an original contribution of reinforecement learning to the field of machine learning.The third part, "A Unified View" present more advanced techniques. The last chapter gives the most important case studies in reinforcement learning including Samuel's Checkers Player and Thesauro's TD-Gammon.The book is very readable and every chapter ends with illustrative exercises (many of them actually are real programming projects!), always useful summary and very valuable bibliographical and historical remarks. Some subchapters are more advanced and therefore marked with '*'. I really recommend first two parts to any student ofd computer science or anyone interested in machine learning and fuzzy computing. The third part is much more advanced but it would be definitely interesting for advanced computer scientists and graduate students.
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