EAN: 9783031004230

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

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. Table of Contents: Markov
Decision Processes   Value Prediction Problems   Control   For Further Exploration

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