Summary of When Do Skills Help Reinforcement Learning? a Theoretical Analysis Of Temporal Abstractions, by Zhening Li et al.
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
by Zhening Li, Gabriel Poesia, Armando Solar-Lezama
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a precise characterization of the utility of deterministic skills in reinforcement learning (RL) for improving performance in deterministic sparse-reward environments with finite action spaces. The authors show theoretically and empirically that RL performance gain from skills is worse in environments where solutions to states are less compressible. Additionally, they find that skills benefit exploration more than they benefit learning from existing experience, and that using unexpressive skills like macroactions may worsen RL performance. This work aims to guide research on automatic skill discovery and help RL practitioners decide when and how to use skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can improve a type of artificial intelligence called reinforcement learning (RL). The authors want to know what makes certain “skills” useful for improving RL performance. They found that the usefulness of these skills depends on the environment, or situation. If the solutions to problems in an environment are easy to compress into smaller pieces, then using skills can actually make things worse. On the other hand, if you’re exploring a new environment and trying to find the best way to solve its challenges, using skills can help. The authors hope that their findings will help people create better AI systems by deciding when and how to use these skills. |
Keywords
» Artificial intelligence » Reinforcement learning
