Summary of Cross-domain Policy Adaptation by Capturing Representation Mismatch, By Jiafei Lyu et al.
Cross-Domain Policy Adaptation by Capturing Representation Mismatch
by Jiafei Lyu, Chenjia Bai, Jingwen Yang, Zongqing Lu, Xiu Li
First submitted to arxiv on: 24 May 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 tackles the challenge of transferring effective policies from a source domain to a target domain with dynamics mismatches in reinforcement learning. The existing methods focus on learning domain classifiers, filtering data, or addressing value discrepancies. Instead, this work adopts a decoupled representation learning perspective, where the representations are learned only in the target domain and measured for deviations from the transitions of the source domain, which can serve as a signal of dynamics mismatch. The authors show that these representation deviations upper bound the performance difference of a given policy between the source and target domains, motivating their use as a reward penalty. The produced representations do not involve policy or value function updates but only serve as a reward penalizer. Experimental results demonstrate strong performance on various tasks in environments with kinematic and morphology mismatch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better policies that can be used in different situations, even if the rules change. When we try to apply what we’ve learned from one place to another, it’s not always easy because the new situation might be very different. The authors have a new approach to solve this problem, which involves learning about the differences between the two places and using that information to make better decisions. They show that their method works well in many situations where there are big changes between the source and target domains. |
Keywords
» Artificial intelligence » Reinforcement learning » Representation learning
