Summary of Offline Behavior Distillation, by Shiye Lei et al.
Offline Behavior Distillation
by Shiye Lei, Sen Zhang, Dacheng Tao
First submitted to arxiv on: 30 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 This paper addresses the challenge of training policies using massive reinforcement learning (RL) data. The authors propose offline behavior distillation (OBD), a method that synthesizes limited expert behavioral data from sub-optimal RL data to enable rapid policy learning. Two naive OBD objectives, DBC and PBC, are introduced, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. The authors then develop an action-value weighted PBC (Av-PBC) objective that achieves a superior distillation guarantee with linear discount complexity O(1/(1-γ)). Experimental results on multiple D4RL datasets demonstrate that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us train better policies using lots of data. It’s like taking a little bit of expert advice and mixing it with some other information to make something new and useful. The authors came up with a way to do this called offline behavior distillation (OBD). They tested different ways to do OBD and found that one method, Av-PBC, works really well. It’s faster and better than the old methods, and it can even work on different types of computers and software. |
Keywords
» Artificial intelligence » Distillation » Generalization » Reinforcement learning
