ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery

TL;DR

This paper introduces ComSD, a novel unsupervised skill discovery method that balances exploration and diversity using contrastive rewards and dynamic weighting, leading to improved downstream task adaptation.

Contribution

ComSD proposes a new intrinsic incentive with a contrastive dynamic reward and a dynamic weighting mechanism to enhance skill diversity and exploration in unsupervised RL.

Findings

Achieves state-of-the-art performance on downstream tasks.

Discovers distinguishable, far-reaching exploration skills.

Effective in complex environments like 2D mazes.

Abstract

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised skill discovery seeks to acquire different useful skills without extrinsic reward via unsupervised Reinforcement Learning (RL), with the discovered skills efficiently adapting to multiple downstream tasks in various ways. However, recent advanced skill discovery methods struggle to well balance state exploration and skill diversity, particularly when the potential skills are rich and hard to discern. In this paper, we propose \textbf{Co}ntrastive dyna\textbf{m}ic \textbf{S}kill \textbf{D}iscovery \textbf{(ComSD)}\footnote{Code and videos: https://github.com/liuxin0824/ComSD} which generates diverse and exploratory unsupervised skills through a novel intrinsic incentive, named contrastive dynamic reward. It contains a particle-based exploration reward to make agents access far-reaching states for exploratory skill acquisition, and a novel contrastive diversity reward to promote the discriminability between different skills. Moreover, a novel dynamic weighting mechanism between the above two rewards is proposed to balance state exploration and skill diversity, which further enhances the quality of the discovered skills. Extensive experiments and analysis demonstrate that ComSD can generate diverse behaviors at different exploratory levels for multi-joint robots, enabling state-of-the-art adaptation performance on challenging downstream tasks. It can also discover distinguishable and far-reaching exploration skills in the challenging tree-like 2D maze.