Algorithms for Reinforcement Learning

丛 书 名：Synthesis lectures on artificial intelligence and machine learning,

作     者：Csaba Szepesvari 

I S B N：(纸本) 9781608454921 

出 版 社：Morgan and Claypool Publishers 

出 版 年：2010年

主 题 词：Markov processes Machine learning Reinforcement learning 

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

摘      要：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 *** 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 *** 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.