CiteWeb id: 19960000069

CiteWeb score: 5654

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.

The publication "Reinforcement learning: a survey" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Computer Science it is included to the Top 1000. Additionally, the publicaiton "Reinforcement learning: a survey" is placed in the Top 100 among other scientific works published in 1996.
Links to full text of the publication: