Reinforcement learning: Computational theory and biological mechanisms
Reinforcement learning: Computational theory and biological mechanisms
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DOI：
10.2976/1.2732246/10.2976/1
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发表时间：
2007-05-01
期刊：
影响因子：
--
通讯作者：
Doya, Kenji
中科院分区：
文献类型：
--
作者：
Doya, Kenji
强化学习是一种计算框架，让主动代理根据标量奖励信号学习行为。代理可以是动物、人类或人工系统，例如机器人或计算机程序。奖励可以是食物、水、金钱或任何衡量代理人表现的标准。强化学习理论是在人工智能界根据动物学习理论的直觉而发展起来的，现在对基底神经节的功能给出了连贯的解释。它现在成为生物学家、工程师和社会科学家可以交流他们的问题和发现的“共同语言”。本文回顾了强化学习的基本理论框架，并讨论了它最近和未来对理解动物行为和人类决策的贡献。
Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent. The theory of reinforcement learning, which was developed in an artificial intelligence community with intuitions from animal learning theory, is now giving a coherent account on the function of the basal ganglia. It now serves as the "common language" in which biologists, engineers, and social scientists can exchange their problems and findings. This article reviews the basic theoretical framework of reinforcement learning and discusses its recent and future contributions toward the understanding of animal behaviors and human decision making.