000			01793nam a2200193 4500
008			220306b ||||| |||| 00| 0 eng d
020			_a9781608454921 (pbk.)
082			_a006.31 _bSZE
100			_aSzepesvári, Csaba _9690
245			_aAlgorithms for reinforcement learning
260			_aWilliston _bMorgan & Claypool _c2010
300			_aviii, 89p.,
500			_ahttps://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=31
520			_aReinforcement 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.
650			_aMachine learning _9481
650			_aMarkov processes _9858
650			_aReinforcement learning _9859
942			_cBK
999			_c7782 _d7782