Reinforcement learning: a survey

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Reinforcement learning: a survey (1996)

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by Leslie Pack Kaelbling , Michael L. Littman , Andrew W. Moore

Venue:	Journal of Artificial Intelligence Research
Citations:	1712 - 25 self

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@ARTICLE{Kaelbling96reinforcementlearning:,
    author = {Leslie Pack Kaelbling and Michael L. Littman and Andrew W. Moore},
    title = {Reinforcement learning: a survey},
    journal = {Journal of Artificial Intelligence Research},
    year = {1996},
    volume = {4},
    pages = {237--285}
}

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Abstract

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Keyphrases

reinforcement learning work described computer-science perspective trial-and-error interaction implemented system broad selection central issue historical basis machine learning practical utility word reinforcement current work markov decision theory dynamic environment empirical model hidden state current method

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