Summary of A Provably Efficient Option-based Algorithm For Both High-level and Low-level Learning, by Gianluca Drappo et al.
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning
by Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a theoretical framework for Hierarchical Reinforcement Learning (HRL) in the option framework, where both high-level and low-level policies are learned. The proposed meta-algorithm alternates between regret minimization algorithms at different temporal abstractions, treating the higher-level problem as a Semi-Markov Decision Process (SMDP) with fixed low-level policies, while learning inner option policies at a lower level. This approach is compared to non-hierarchical finite-horizon problems, allowing for characterization of when HRL is provably preferable even without pre-trained options. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how computers can make good decisions over time by combining two types of learning: high-level and low-level policies. The algorithm learns both levels simultaneously, which is better than previous approaches that only learned one level at a time. This is important because it means we can use computers to solve complex problems in areas like robotics, finance, or healthcare. |
Keywords
* Artificial intelligence * Reinforcement learning
