Abstract
Reinforcement learning is a machine learning method that enables the learning of optimal behavior in challenging or uncertain environments. Optimal behavior in this case can be defined as the set of sequential decisions that result in the achievement of a goal or the best possible outcome. This learning process can be regarded as a process of trial-and-error, which is coupled with feedback provided from the environment that indicates the utility of the outcome. This learning method ultimately attempts to learn a mapping between actions and outcomes.
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Gatti, C. (2015). Introduction. In: Design of Experiments for Reinforcement Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-12197-0_1
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DOI: https://doi.org/10.1007/978-3-319-12197-0_1
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