Reinforcement learning has become a wide and deep conduit that links ideas and results in computer science, statistics, control theory and economics to psychological data on animal and human decision-making, and the neural basis of choice. There is a ready and free flow of ideas among these disciplines, providing a powerful foundation for exploring some of the complexities of both normal and abnormal behaviours. I will outline some of the happy circumstances that led us to this point; discuss current computational, algorithmic and implementational themes; and provide some pointers to the future.
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