publication . Preprint . Other literature type . Article . 1996

Reinforcement learning: a survey

L. P. Kaelbling; M. L. Littman; A. W. Moore;
Open Access English
  • Published: 01 May 1996
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, ...
free text keywords: Computer Science - Artificial Intelligence, Artificial Intelligence
Related Organizations
Funded by
NSF| Presidential Faculty Fellows: Learning for Autonomous Agents
  • Funder: National Science Foundation (NSF)
  • Project Code: 9453383
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems

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