Reinforcement learning is a learning paradigm concerned with learning to control a system so as  to maximize a numerical performance measure that expresses a long-term objective.What distinguishes  reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through  influencing the future state of the controlled system. Thus, time plays a special role. The goal in  reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large  number of practical applications that it can be used to address, ranging from problems in artificial  intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

Algorithms for Reinforcement Learning PDF

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