Summary of Offline Reinforcement Learning: Role Of State Aggregation and Trajectory Data, by Zeyu Jia et al.
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
by Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
First submitted to arxiv on: 25 Mar 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 This AI research paper abstract proposes a new framework for offline reinforcement learning with value function realizability but without Bellman completeness. The authors investigate whether bounded concentrability coefficient along with trajectory-based offline data admits polynomial sample complexity, specifically focusing on the task of offline policy evaluation. Their primary findings are threefold: firstly, they show that the sample complexity is governed by the concentrability coefficient in an aggregated Markov Transition Model; secondly, they demonstrate that this coefficient may grow exponentially with the horizon length even when the original MDP has a small coefficient and the offline data is admissible; and thirdly, they prove that there is a generic reduction converting hard instances with admissible data to those with trajectory data. These findings unify and generalize previous work in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how computers can learn from past experiences without having access to real-time feedback. The authors are trying to solve a tricky problem called offline policy evaluation, where they want to predict how well an algorithm will perform in different situations. They discovered that this problem depends on two things: the quality of the data and the complexity of the situation. If the data is good but the situation is complex, it’s harder for the computer to make accurate predictions. The authors also found that using more data doesn’t always help, because the complexity of the situation can still be a major obstacle. |
Keywords
* Artificial intelligence * Reinforcement learning
