Summary of Offline Reinforcement Learning with Combinatorial Action Spaces, by Matthew Landers et al.
Offline Reinforcement Learning With Combinatorial Action Spaces
by Matthew Landers, Taylor W. Killian, Hugo Barnes, Thomas Hartvigsen, Afsaneh Doryab
First submitted to arxiv on: 28 Oct 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 This paper tackles the challenge of reinforcement learning in situations where multiple sub-actions need to be executed simultaneously, resulting in an exponentially large action space. The authors highlight the difficulties in this scenario, including the limitations of offline data and the assumption of sub-action independence made by current methods. To address these challenges, they propose a new approach called Branch Value Estimation (BVE), which can learn to evaluate only a small subset of actions at each timestep while effectively capturing sub-action dependencies. The authors demonstrate the effectiveness of BVE in outperforming state-of-the-art methods across a range of action space sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways for computers to make good decisions when they need to do multiple things at once. Right now, it’s hard to teach computers how to do this because there are so many possibilities. The authors want to make it easier by creating a new way to learn that can take into account the connections between these different actions. They tested their idea and found that it works better than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
