Introduction to Hierarchical Reinforcement Learning🔗
Abstract🔗
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. In this talk, I will review the main ideas of these computational approaches and present some recent advances in this field. In addition to computational results, I will draw some connections between the algorithms’ hierarchical reinforcement learning approaches and existing similar models of human and animal decision making.