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- Title
Reinforcement learning improves behaviour from evaluative feedback.
- Authors
Littman, Michael L.
- Abstract
Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.
- Subjects
REINFORCEMENT learning; MACHINE learning; FEEDBACK control systems; ARTIFICIAL intelligence; GENERALIZATION; DECISION making; BAYESIAN analysis; MARKOV processes
- Publication
Nature, 2015, Vol 521, Issue 7553, p445
- ISSN
0028-0836
- Publication type
Article
- DOI
10.1038/nature14540