Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill Learning

• URL: http://arxiv.org/abs/2307.02728v2
• Date: Tue, 3 Oct 2023 18:24:31 GMT
• Title: Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill Learning
• Authors: Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum, George Konidaris
• Abstract summary: General purpose agents will require large repertoires of skills. We introduce a new framework, Hierarchical Empowerment, that makes computing empowerment more tractable. In a popular ant navigation domain, our four level agents are able to learn skills that cover a surface area over two orders of magnitude larger than prior work.
• Score: 65.41865750258775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: General purpose agents will require large repertoires of skills. Empowerment -- the maximum mutual information between skills and states -- provides a pathway for learning large collections of distinct skills, but mutual information is difficult to optimize. We introduce a new framework, Hierarchical Empowerment, that makes computing empowerment more tractable by integrating concepts from Goal-Conditioned Hierarchical Reinforcement Learning. Our framework makes two specific contributions. First, we introduce a new variational lower bound on mutual information that can be used to compute empowerment over short horizons. Second, we introduce a hierarchical architecture for computing empowerment over exponentially longer time scales. We verify the contributions of the framework in a series of simulated robotics tasks. In a popular ant navigation domain, our four level agents are able to learn skills that cover a surface area over two orders of magnitude larger than prior work.

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