Summary of Offline Rl Via Feature-occupancy Gradient Ascent, by Gergely Neu et al.
Offline RL via Feature-Occupancy Gradient Ascent
by Gergely Neu, Nneka Okolo
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel offline reinforcement learning algorithm for large infinite-horizon discounted Markov Decision Processes (MDPs) with linearly realizable reward and transition models. The proposed method, inspired by gradient ascent in the space of feature occupancies, achieves strong computational and sample complexity guarantees under minimal data coverage assumptions. This approach scales optimally with desired accuracy levels and requires no prior knowledge of coverage ratios. The algorithm outperforms existing methods in this setting, making it a significant contribution to offline reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in artificial intelligence called offline reinforcement learning. It helps computers learn from experience without needing constant feedback. The researchers develop a new way for computers to learn and make decisions in complex situations. Their method is more efficient than previous methods and can work with limited data. This makes it useful for real-world applications where computers need to make decisions quickly and accurately. |
Keywords
» Artificial intelligence » Reinforcement learning
