Lipschitz-constrained Unsupervised Skill Discovery
- URL: http://arxiv.org/abs/2202.00914v1
- Date: Wed, 2 Feb 2022 08:29:04 GMT
- Title: Lipschitz-constrained Unsupervised Skill Discovery
- Authors: Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim
- Abstract summary: Lipschitz-constrained Skill Discovery (LSD) encourages the agent to discover more diverse, dynamic, and far-reaching skills.
LSD outperforms previous approaches in terms of skill diversity, state space coverage, and performance on seven downstream tasks.
- Score: 91.51219447057817
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the problem of unsupervised skill discovery, whose goal is to learn
a set of diverse and useful skills with no external reward. There have been a
number of skill discovery methods based on maximizing the mutual information
(MI) between skills and states. However, we point out that their MI objectives
usually prefer static skills to dynamic ones, which may hinder the application
for downstream tasks. To address this issue, we propose Lipschitz-constrained
Skill Discovery (LSD), which encourages the agent to discover more diverse,
dynamic, and far-reaching skills. Another benefit of LSD is that its learned
representation function can be utilized for solving goal-following downstream
tasks even in a zero-shot manner - i.e., without further training or complex
planning. Through experiments on various MuJoCo robotic locomotion and
manipulation environments, we demonstrate that LSD outperforms previous
approaches in terms of skill diversity, state space coverage, and performance
on seven downstream tasks including the challenging task of following multiple
goals on Humanoid. Our code and videos are available at
https://shpark.me/projects/lsd/.
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