Summary of Unsupervised-to-online Reinforcement Learning, by Junsu Kim et al.
Unsupervised-to-Online Reinforcement Learning
by Junsu Kim, Seohong Park, Sergey Levine
First submitted to arxiv on: 27 Aug 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 proposed unsupervised-to-online reinforcement learning (U2O RL) framework is an alternative to the traditional offline-to-online RL approach. U2O RL replaces domain-specific supervised offline RL with unsupervised offline RL, enabling reusing a single pre-trained model for multiple downstream tasks. The proposed recipe bridges task-agnostic unsupervised offline skill-based policy pre-training and supervised online fine-tuning. The authors empirically demonstrate the effectiveness of U2O RL in nine state-based and pixel-based environments, achieving strong performance that matches or outperforms previous approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline-to-online reinforcement learning (RL) is a promising approach for data-driven decision-making. However, it has drawbacks: requiring domain-specific offline RL pre-training for each task and being often brittle in practice. A new framework called unsupervised-to-online RL (U2O RL) solves these problems by replacing supervised offline RL with unsupervised offline RL. This allows reusing a single pre-trained model for multiple tasks, leading to better representations and performance. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Supervised » Unsupervised
