Summary of Return Augmented Decision Transformer For Off-dynamics Reinforcement Learning, by Ruhan Wang et al.
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
by Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); 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 In this study, researchers develop a novel approach to offline reinforcement learning (RL) that leverages data from an easily accessible source domain to improve policy learning in a target domain with limited data. The key innovation is the return-conditioned supervised learning (RCSL) framework, which relies on the decision transformer (DT) model to predict actions conditioned on desired return guidance and complete trajectory history. The team proposes the Return Augmented Decision Transformer (RADT) method, which aligns the return distribution in the source domain with that in the target domain. Theoretical analysis shows that RCSL policies learned from RADT achieve the same level of suboptimality as without a dynamics shift. Two practical implementations, RADT-DARA and RADT-MV, are introduced, demonstrating improved performance over dynamic programming-based methods in off-dynamics RL scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers created a new way to use data from one place to help learn better policies in another place where there isn’t much data. They used something called return-conditioned supervised learning (RCSL) and a special kind of model called the decision transformer (DT). This model helps make good choices based on what you want to happen and what has happened so far. The team made a new method, RADT, which makes sure the return (or reward) in the easy place is the same as it would be in the hard place. They showed that this works well and even compared it to other ways of doing things. The results are very good! |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised » Transformer
