Summary
Reinforcement-Learning is learning how to best-react to situations, through trial and error. In the Machine-Learning community Reinforcement-Learning is researched with respect to artificial (machine) decision-makers, referred to as agents. The agents are assumed to be situated within an environment which behaves as a Markov Decision Process. This chapter provides a brief introduction to Reinforcement-Learning, and establishes its relation to Data-Mining. Specifically, the Reinforcement-Learning problem is defined; a few key ideas for solving it are described; the relevance to Data-Mining is explained; and an instructive example is presented.
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Maimon, O., Cohen, S. (2009). A Review of Reinforcement Learning Methods. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_20
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