Algorithms for Reinforcement Learning (Paperback)

Csaba Szepesvari

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商品描述

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.

商品描述(中文翻譯)

強化學習是一種學習範式,關注於學習如何控制一個系統,以最大化表達長期目標的數值效能度量。強化學習與監督學習的區別在於,只給予學習者關於其預測的部分反饋。此外,這些預測可能通過影響受控系統的未來狀態而產生長期影響。因此,時間在其中扮演了特殊的角色。強化學習的目標是發展高效的學習算法,並理解這些算法的優點和限制。強化學習非常有趣,因為它可以應用於眾多實際問題,從人工智慧到運籌學或控制工程等領域。在本書中,我們專注於那些建立在動態規劃強大理論基礎上的強化學習算法。我們提供了一個相當全面的學習問題目錄,描述了核心思想,介紹了大量最新的算法,並討論了它們的理論特性和限制。