Summary of Domain Adaptation For Offline Reinforcement Learning with Limited Samples, by Weiqin Chen et al.
Domain Adaptation for Offline Reinforcement Learning with Limited Samples
by Weiqin Chen, Sandipan Mishra, Santiago Paternain
First submitted to arxiv on: 22 Aug 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 framework theoretically explores the impact of weights assigned to each dataset on offline reinforcement learning performance. It establishes performance bounds and the existence of an optimal weight, which can be computed in closed form under simplifying assumptions. The results depend on the quality of the source dataset and the number of samples from the target dataset. Offline RL algorithms rely on high-quality and large target datasets, but real-world applications often have limited sample availability. Domain adaptation leveraging auxiliary samples from related source datasets (such as simulators) can be beneficial. However, trading off the source and target datasets while ensuring provable theoretical guarantees remains an open challenge. This paper fills this gap by proposing a framework that theoretically explores the optimal way to combine source and target datasets in offline RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning learns effective policies from static target data. But what if we only have a few examples? That’s where domain adaptation comes in! It helps by using extra information from related simulations or games. But how do we mix this extra info with the real-world data to get the best results? This paper figures out how to make that happen, and it gives us rules to follow so our algorithms work well even when there isn’t much data. |
Keywords
» Artificial intelligence » Domain adaptation » Reinforcement learning
