Reinforcement Learning: An Introduction
Reinforcement Learning: An Introduction
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DOI：
10.1109/tnn.1998.712192
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发表时间：
1998
期刊：
影响因子：
--
通讯作者：
R. S. Sutton;A. Barto
中科院分区：
文献类型：
--
作者：
R. S. Sutton;A. Barto
强化学习是人工智能中最活跃的研究领域之一，是一种学习的计算方法，通过这种方法，智能体在与复杂的不确定环境交互时试图最大化它所获得的奖励总量。在《强化学习》一书中，Richard萨顿和Andrew Barto对强化学习的关键思想和算法进行了清晰而简单的描述。他们的讨论范围从该领域的知识基础的历史到最近的发展和应用。唯一必要的数学背景是熟悉概率的基本概念。本书分为三个部分。第一部分从马尔可夫决策过程的角度定义了强化学习问题。第二部分提供了基本的解决方法：动态规划，蒙特卡罗方法和时间差学习。第三部分介绍了解决方案方法的统一视图，并结合了人工神经网络，资格跟踪和规划;最后两章介绍了案例研究，并考虑了强化学习的未来。
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.