Learning Diverse Options via InfoMax Termination Critic

Yuji Kanagawa, Tomoyuki Kaneko

Keywords: reinforcement learning, hierachical reinforcement learning, options, life long reinforcement learning, skill transfer

Abstract: We consider the problem of autonomously learning reusable temporally extended actions, or options, in reinforcement learning. While options can speed up transfer learning by serving as reusable building blocks, learning reusable options for unknown task distribution remains challenging. Motivated by the recent success of mutual information (MI) based skill learning, we hypothesize that more diverse options are more reusable. To this end, we propose a method for learning termination conditions of options by maximizing MI between options and corresponding state transitions. We derive a scalable approximation of this MI maximization via gradient ascent, yielding the InfoMax Termination Critic (IMTC) algorithm. Our experiments demonstrate that IMTC significantly improves the diversity of learned options without rewards, combined with an intrinsic option learning method. Moreover, we test the reusability of learned options by transferring options into various tasks, confirming that IMTC helps quick adaptation, especially in complex domains where an agent needs to manipulate objects.

One-sentence Summary: We aim to learn diverse options via MI maximization to get reusable behavioral blocks.