Summary of Order-optimal Instance-dependent Bounds For Offline Reinforcement Learning with Preference Feedback, by Zhirui Chen and Vincent Y. F. Tan
Order-Optimal Instance-Dependent Bounds for Offline Reinforcement Learning with Preference Feedback
by Zhirui Chen, Vincent Y. F. Tan
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Statistics Theory (math.ST); 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 algorithm called RL-LOW (Reinforcement Learning with Locally Optimal Weights) for offline reinforcement learning with preference feedback. The goal is to minimize the simple regret, which is a measure of how much better the optimal policy is than the actual policy used. The algorithm achieves a simple regret of exponential order in terms of the number of data samples and an instance-dependent hardness quantity. The paper also derives a lower bound for offline RL with preference feedback, showing that the upper and lower bounds match order-wise. This demonstrates the optimality of RL-LOW. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to make decisions based on past experiences without needing more data. It’s like trying to figure out what’s the best action to take in a game based on how others have played before. The algorithm, called RL-LOW, helps us find the best action by considering how well each option performed in the past. This can be useful in situations where we don’t have time to gather more data or when our decisions need to be private. |
Keywords
» Artificial intelligence » Reinforcement learning
