Summary of Reward-relevance-filtered Linear Offline Reinforcement Learning, by Angela Zhou
Reward-Relevance-Filtered Linear Offline Reinforcement Learning
by Angela Zhou
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 explores offline reinforcement learning with linear function approximation in a decision-theoretic setting where the transitions can be modeled as a sparse component affecting the reward. The researchers develop a method to filter out irrelevant state-action values by applying a modified thresholded lasso algorithm to the least-squares policy evaluation. This approach provides theoretical guarantees for the sample complexity, which depends only on the size of the sparse component. The method is applicable in scenarios where the optimal policy and state-action value function rely solely on the sparse component. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is a type of machine learning that helps computers learn from experiences without needing to interact with the environment anymore. In this study, scientists found a way to make this process work better by focusing only on the most important parts of the data. This is helpful when there’s too much information and it gets in the way of making good decisions. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
