Summary of The Role Of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation, by Noah Golowich and Ankur Moitra
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
by Noah Golowich, Ankur Moitra
First submitted to arxiv on: 17 Jun 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 The paper proposes an offline reinforcement learning algorithm with linear function approximation, which assumes that the Markov decision process has low inherent Bellman error. This assumption allows for value iteration to succeed. The algorithm guarantees a policy whose value is at least as good as any policy well-covered by the dataset, even in cases where the Bellman completeness is 0. The paper’s contributions include a computationally efficient algorithm and the first known guarantee under single-policy coverage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to make better decisions using machines that learn from experience. It creates an efficient way for machines to figure out good policies when they don’t have all the information they need. This is important because it can help us use machines to solve problems in areas like healthcare or finance, where we want to be sure our decisions are as good as possible. |
Keywords
» Artificial intelligence » Reinforcement learning
