Year: 2007
Type: article
Abstract: 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.
Source: HFSP journal
DOI: 10.2976/1.2732246
Author: Kenji Doya
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2091789872