Reinforcement Learning - The Springer International Series in Engineering and Computer Science 173 (Hardback)Richard S. Sutton (editor)
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Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement).
Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Number of pages: 172
Weight: 960 g
Dimensions: 235 x 155 x 11 mm
Edition: Reprinted from `MACHINE LEARNING', 8: 3/4, 19