Computational Reinforcement-learning in Biological and Non-biological Systems
Reinforcement-learning concerns the computational principles by which animals and artificial agents can learn to select actions in their environment in order to maximize their future rewards. Over the past 50 years there has been a rich interplay between the development and application of reinforcement-learning models in artificial intelligence, and the investigation of reinforcement-learning in biological systems, including humans. This course will review this rich literature, covering the psychology of animal-learning, the neurobiology of reward and reinforcement, and the theoretical basis and application of reinforcement-learning models to biological and non-biological systems. Not offered 2022-23.
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