Summary of Skild: Unsupervised Skill Discovery Guided by Factor Interactions, By Zizhao Wang et al.
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
by Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Unsupervised skill discovery is an exciting area of research in artificial intelligence that enables agents to learn reusable skills through autonomous interaction with environments. Existing approaches focus on encouraging distinguishable behaviors that cover diverse states, but this can lead to simple skills that are not ideal for solving downstream tasks in complex environments. This paper introduces Skill Discovery from Local Dependencies (Skild), a novel approach that leverages state factorization as a natural inductive bias to guide the skill learning process. Skild’s key intuition is that skills that induce diverse interactions between state factors are often more valuable for solving downstream tasks. To this end, Skild develops a novel skill learning objective that explicitly encourages the mastering of skills that effectively induce different interactions within an environment. We evaluate Skild in several domains with challenging long-horizon sparse reward tasks, including a realistic simulated household robot domain, where Skild successfully learns skills with clear semantic meaning and shows superior performance compared to existing unsupervised reinforcement learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if robots or AI agents could learn new skills on their own, without being told what to do. This is called unsupervised skill discovery. Currently, there are ways for AI agents to learn new skills by doing different things in an environment. However, this approach has some limitations. It can be hard for the agent to learn skills that cover all possible situations in a complex environment. In this paper, we introduce a new way to learn skills called Skill Discovery from Local Dependencies (Skild). Skild is designed to help AI agents learn more valuable skills by encouraging them to interact with different parts of an environment. We tested Skild in several scenarios and found that it works well, even better than other existing methods. |
Keywords
» Artificial intelligence » Reinforcement learning » Unsupervised
