Summary of A Natural Extension to Online Algorithms For Hybrid Rl with Limited Coverage, by Kevin Tan et al.
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage
by Kevin Tan, Ziping Xu
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 In this paper, researchers explore Hybrid Reinforcement Learning (RL) that combines online and offline data to improve learning outcomes. The study demonstrates that existing algorithms impose unnecessary assumptions on offline datasets and proposes a novel approach that “fills in the gaps” in the offline dataset by exploring unvisited states and actions. This approach achieves similar provable gains from hybrid data without requiring single-policy concentrability of the offline dataset. The researchers also introduce DISC-GOLF, a modified algorithm that demonstrates provable gains over online-only and offline-only RL in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hybrid Reinforcement Learning (RL) combines two types of data: online and offline. This paper shows how to use this combined data to make better decisions. Usually, we think about online data as something new and important, while offline data is like a reference book that helps us learn. But in reality, these two types of data can work together to help us make better choices. The researchers come up with a new way to combine these data, called DISC-GOLF, which makes our decisions more efficient and effective. This is good news for people who want to use RL to solve real-world problems. |
Keywords
* Artificial intelligence * Reinforcement learning
