Summary of Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy, by Keru Chen et al.
Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy
by Keru Chen, Honghao Wei, Zhigang Deng, Sen Lin
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recent advancements in online safe reinforcement learning (RL) have been hindered by the high costs and risks involved in extensive environment interactions. Offline safe RL, which learns policies from static datasets, addresses this issue but is limited due to data quality and out-of-distribution action challenges. The introduction of offline-to-online (O2O) RL has shown promise, but existing O2O algorithms do not work well in the safe RL setting due to unique challenges such as erroneous Q-estimations and Lagrangian mismatch. To address these issues, we introduce Marvel, a novel framework for O2O safe RL that comprises Value Pre-Alignment and Adaptive PID Control components. Marvel significantly outperforms existing baselines in both reward maximization and safety constraint satisfaction. This work has the potential to advance the field towards more efficient and practical safe RL solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer programs learn how to make good decisions without getting into trouble. Right now, it’s hard for these programs to learn because they have to interact with the world in a way that might get them into trouble. The researchers came up with a new idea called Marvel that helps these programs learn faster and safer. They tested Marvel and found that it did much better than other methods at making good decisions while avoiding trouble. This could help us make more useful computer programs that can help us without causing problems. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
