Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes

Sophia Sanborn, Michael Chang, Sergey Levine, Thomas Griffiths

Abstract: Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks. We consider an alternative perspective based on identifying behavioral `motifs'---repeated action sequences that can be compressed to yield a compact code of action trajectories. We present a method for iteratively compressing action trajectories to learn nested behavioral hierarchies of arbitrary depth, with actions of arbitrary length. The learned temporally extended actions provide new action primitives that can participate in deeper hierarchies as the agent learns. We demonstrate the relevance of this approach for tasks with non-trivial hierarchical structure and show that the approach can be used to accelerate learning in recursively more complex tasks through transfer.

Keywords: hierarchical reinforcement learning, unsupervised learning, compression