Summary of Dataset Clustering For Improved Offline Policy Learning, by Qiang Wang et al.
Dataset Clustering for Improved Offline Policy Learning
by Qiang Wang, Yixin Deng, Francisco Roldan Sanchez, Keru Wang, Kevin McGuinness, Noel O’Connor, Stephen J. Redmond
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 paper studies the impact of multi-behavior datasets on offline policy learning in reinforcement learning. It finds that policies learned from uni-behavior datasets typically outperform those learned from multi-behavior datasets, despite the former having fewer examples and less diversity. To address this issue, the authors propose a behavior-aware deep clustering approach that partitions multi-behavior datasets into several uni-behavior subsets, benefiting downstream policy learning. This approach is flexible, effective, and adaptable to various continuous control task datasets. The results demonstrate high clustering accuracy with an average Adjusted Rand Index of 0.987. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline policy learning tries to find the best way to make decisions without having more interactions with the environment. The quality of the data used for training is very important because it affects how well the learned policy performs. This paper looks at what happens when we have a dataset that was collected using many different policies, each doing things in its own way. It finds that the best policies are often those trained on datasets where only one policy was used to collect the data, even if there is less of it and less variety. To fix this problem, the authors suggest using an approach that groups similar behaviors together from the multi-behavior dataset, making it easier for later policy learning. |
Keywords
* Artificial intelligence * Clustering * Reinforcement learning
