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Hierarchical reinforcement learning

Thursday, October 11, 2018 (All day)

Doina Precup's research interests are in the areas of reinforcement learning, deep learning, time series analysis, and diverse applications. In this talk, Dr. Precup reviews how hierarchical reinforcement learning refers to a class of computational methods that enable artificial agents that train using reinforcement learning to act, learn and plan at different levels of temporal abstraction. She draws connections between the algorithms’ hierarchical reinforcement learning approaches and existing similar models of human and animal decision making.

Doina Precup
Associate Professor
Computer Science
McGill University and DeepMind Montreal