Summary of A Primal-dual Algorithm For Offline Constrained Reinforcement Learning with Linear Mdps, by Kihyuk Hong et al.
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
by Kihyuk Hong, Ambuj Tewari
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a primal dual algorithm for offline reinforcement learning (RL) with linear Markov Decision Processes (MDPs) under an infinite-horizon discounted setting. The goal is to learn a policy that maximizes the expected discounted cumulative reward using a pre-collected dataset, without requiring uniform data coverage assumptions or being computationally inefficient. The proposed algorithm achieves sample complexity of O(ε^-2) with partial data coverage assumption, improving upon previous works that require O(ε^-4) samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to make good decisions using past experiences. They want to find the best way to get a reward by trying different actions and learning from what happened before. The researchers came up with a new method that can do this quickly and efficiently, even if they don’t have all the data they need. This is important because it could help machines learn how to make better decisions in situations where they don’t have complete information. |
Keywords
* Artificial intelligence * Reinforcement learning
