Summary of Benchmarks For Reinforcement Learning with Biased Offline Data and Imperfect Simulators, by Ori Linial et al.
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
by Ori Linial, Guy Tennenholtz, Uri Shalit
First submitted to arxiv on: 30 Jun 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 A novel approach in reinforcement learning combines offline data with imperfect simulators to overcome limitations in real-world exploration. The study highlights four principal challenges: simulator modeling error, partial observability, state and action discrepancies, and hidden confounding. To address these issues, the authors introduce “Benchmarks for Mechanistic Offline Reinforcement Learning” (B4MRL), a set of dataset-simulator benchmarks that provide a foundation for future research in this area. The paper’s findings emphasize the importance of such benchmarks in advancing offline reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning trains agents without real-world exploration, but biases can occur due to data distribution shifts and incomplete environment representation. To overcome these issues, hybrid methods combine simulators with grounded offline data. However, simulator construction is challenging due to complex systems and missing information. The study identifies four key challenges in combining offline data with imperfect simulators and introduces benchmarks to drive future research. |
Keywords
* Artificial intelligence * Reinforcement learning
