Summary of Upper and Lower Bounds For Distributionally Robust Off-dynamics Reinforcement Learning, by Zhishuai Liu et al.
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning
by Zhishuai Liu, Weixin Wang, Pan Xu
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 Off-dynamic Reinforcement Learning (RL) involves training policies in one environment and deploying them in another. To address this challenge, we develop a novel algorithm called We-DRIVE-U that learns robust policies under distributionally robust Markov decision processes (DRMDPs). Our approach improves upon the state-of-the-art by (dH/{1/,H}) in terms of average suboptimality, where K is the number of episodes, H is the horizon length, d is the feature dimension, and is the uncertainty level. We also construct a novel hard instance and derive an information-theoretic lower bound, indicating that our algorithm is near-optimal up to (). Additionally, our ‘rare-switching’ design reduces policy switches and oracle calls, improving computational efficiency. Our approach has implications for applications such as robotics and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine teaching a computer how to make decisions in one situation, but then having it make those same decisions in a slightly different situation. This is called off-dynamic Reinforcement Learning. Researchers developed a new way to teach computers to make good decisions even when the situation changes. They created an algorithm that can adapt to uncertainty and learn from experience. This algorithm is better than previous ones at finding the best solution, and it uses less computational power. The results have implications for areas like robotics and finance. |
Keywords
* Artificial intelligence * Reinforcement learning
