Summary of Samg: Offline-to-online Reinforcement Learning Via State-action-conditional Offline Model Guidance, by Liyu Zhang et al.
SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance
by Liyu Zhang, Haochi Wu, Xu Wan, Quan Kong, Ruilong Deng, Mingyang Sun
First submitted to arxiv on: 24 Oct 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 Medium Difficulty summary: Offline-to-online (O2O) reinforcement learning (RL) pre-trains models on offline data and refines policies through online fine-tuning. Existing O2O RL algorithms typically require maintaining offline datasets to mitigate out-of-distribution (OOD) data effects, limiting efficiency in exploiting online samples. To address this deficiency, we introduce State-Action-Conditional Offline Model Guidance (SAMG), which freezes the pre-trained offline critic for compact offline understanding and eliminates offline dataset retraining needs. SAMG incorporates the frozen offline critic with an online target critic weighted by a state-action-adaptive coefficient, updated adaptively during training. This paradigm is theoretically shown to have good optimality and lower estimation error, empirically outperforming state-of-the-art O2O RL algorithms on the D4RL benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Reinforcement learning (RL) helps machines learn from experiences. Sometimes, we need to use data collected before to improve our machine’s decision-making skills. This paper presents a new way to do this called State-Action-Conditional Offline Model Guidance (SAMG). SAMG makes it easier for machines to adjust their decisions based on new information without needing to re-train using old data. The approach is tested and shown to be better than previous methods in certain scenarios. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning
