Reinforcement Learning Repository at MSU

Topics: Hierarchical Methods

These refer to algorithms that operate by solving MDP's at a series of abstraction levels, where higher levels in the abstraction hierarchy ignore details represented at lower levels. Hierarchical methods represent a strategy for dealing with very large state spaces. The motivation for the use of hierarchical models in RL is the goal of faster learning (perhaps at the expense of slight sub-optimality in performance) through decomposition of a task into a collection of simpler subtasks.