Summary of Approximate Equivariance in Reinforcement Learning, by Jung Yeon Park et al.
Approximate Equivariance in Reinforcement Learning
by Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L.S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters
First submitted to arxiv on: 6 Nov 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 proposes approximately equivariant algorithms for reinforcement learning (RL) that can handle situations where only approximate symmetry is present in the task. By developing novel RL architectures using relaxed group convolutions, the authors demonstrate improved sample efficiency and generalization compared to existing approaches when there are approximate symmetries. The proposed methods outperform prior work in various continuous control domains and stock trading with real financial data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better by creating new ways for computers to make decisions based on rules that might not be exact, but close enough. It’s like trying to find the perfect recipe for a cake – you don’t need it to be exact, just good enough! The authors show that this approach works well in many situations where things aren’t perfectly symmetrical. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
