Summary of Robust Offline Reinforcement Learning For Non-markovian Decision Processes, by Ruiquan Huang et al.
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes
by Ruiquan Huang, Yingbin Liang, Jing Yang
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A new approach to robust offline reinforcement learning (RL) is proposed for non-Markovian decision processes with low-rank structures. The method, featuring dataset distillation and a lower confidence bound design, enables finding an -optimal robust policy using O(1/^2) offline samples. This algorithm is extended to handle nominal models without specific structure, achieving polynomial sample efficiency under various uncertainty sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning can be improved by considering different environments within a set of possible scenarios. A new method is developed for non-Markovian decision processes with low-rank structures. The approach uses data distillation and confidence bounds to find the best policy. This method works well even when we don’t know all the details about how the environment might change. |
Keywords
» Artificial intelligence » Distillation » Reinforcement learning
