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.
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The author appreciates his discussions with his colleagues that led to this synthesis of current work.
The author declares no competing financial interests.
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Littman, M. Reinforcement learning improves behaviour from evaluative feedback. Nature 521, 445–451 (2015). https://doi.org/10.1038/nature14540
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