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Adaptive Representations for Reinforcement Learning
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This book presents the main results new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks an effective control policy for tackling a sequential decision task. Unlike in supervised learning, the agent never sees examples of correct or incorrect behavior but receives only a reward signal as feedback. This book also introduces a novel method for devising input representations. In particular, it presents a way to find a minimal set of features sufficient to describe the agent s current state, a challenge known as the feature selection problem. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially§improve performance over methods with manual representations.
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