Summary of Optimistic Critic Reconstruction and Constrained Fine-tuning For General Offline-to-online Rl, by Qin-wen Luo et al.
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
by Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang
First submitted to arxiv on: 25 Dec 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 authors propose a novel approach for offline-to-online (O2O) reinforcement learning, enabling the rapid improvement of pre-trained policies with limited online interactions. They identify evaluation and improvement mismatches between offline datasets and online environments, which hinder direct policy application to fine-tuning. The proposed method simultaneously handles these mismatches by re-evaluating a pessimistic critic trained on the offline dataset in an optimistic way, calibrating the misaligned critic with the reliable offline actor, and then performing constrained fine-tuning to combat distribution shift during online learning. The approach achieves stable and efficient performance improvement on multiple simulated tasks, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to use old training data for better decisions in real-world situations. It’s like using a good first impression as a starting point to make even better choices later on. The authors found that when moving from offline (training) to online (real-life), there are two main problems: evaluating the performance of what we’ve learned so far and making sure our learning doesn’t get worse because of this change. To solve these issues, they came up with a way to improve the evaluation process and make the learning more reliable. This helps us achieve better results in new situations. The approach works well for different simulated tasks, showing it’s a useful tool for decision-making. |
Keywords
» Artificial intelligence » Fine tuning » Online learning » Reinforcement learning
