Summary of Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency From Shifted-dynamics Data, by Chengrui Qu et al.
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
by Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You, Adam Wierman
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 hybrid transfer reinforcement learning (HTRL) setting enables agents to learn in a target environment while leveraging offline data from a source environment with shifted dynamics. The study investigates the use of historical data to improve sample efficiency and demonstrates that general shifted-dynamics data does not necessarily reduce sample complexity. However, by incorporating prior information on the degree of the dynamics shift, the HySRL algorithm achieves problem-dependent sample complexity and outperforms pure online RL methods. Experimental results show that HySRL surpasses state-of-the-art online RL baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to teach machines how to learn from old data. They wanted to see if they could use information from the past to help machines learn faster and more efficiently in the present. The scientists discovered that just having some old data isn’t enough – you need to know how different the current situation is compared to the past. By using this knowledge, they created an algorithm called HySRL that can learn quickly and accurately even when faced with new challenges. |
Keywords
* Artificial intelligence * Reinforcement learning
