![[2602.21072] Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21072] Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
Summary
The paper presents Localized Dynamics-Aware Domain Adaptation (LoDADA) for off-dynamics offline reinforcement learning, enhancing data selection by focusing on localized dynamics discrepancies.
Why It Matters
This research addresses significant challenges in reinforcement learning, particularly in leveraging source data effectively when transitioning between different dynamics. By improving data selection strategies, it can enhance the performance of RL applications in various domains, making it relevant for both academic and practical advancements in AI.
Key Takeaways
- LoDADA clusters source and target transitions to identify localized dynamics discrepancies.
- The method improves data selection efficiency by filtering out less relevant source transitions.
- Empirical results demonstrate superior performance compared to existing offline RL methods.
- The approach is scalable and avoids the computational costs of traditional filtering methods.
- Theoretical insights support the effectiveness of localized adaptation in RL.
Computer Science > Machine Learning arXiv:2602.21072 (cs) [Submitted on 24 Feb 2026] Title:Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning Authors:Zhangjie Xia, Yu Yang, Pan Xu View a PDF of the paper titled Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning, by Zhangjie Xia and 2 other authors View PDF HTML (experimental) Abstract:Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics. Existing methods typically address dynamics mismatch either globally over the state space or via pointwise data filtering; these approaches can miss localized cross-domain similarities or incur high computational cost. We propose Localized Dynamics-Aware Domain Adaptation (LoDADA), which exploits localized dynamics mismatch to better reuse source data. LoDADA clusters transitions from source and target datasets and estimates cluster-level dynamics discrepancy via domain discrimination. Source transitions from clusters with small discrepancy are retained, while those from clusters with large discrepancy are filtered out. This yields a fine-grained and scalable data selection strategy that avoids overly coarse global assumptions and expensive per-sample filtering. We provide theoretical insights and extensive experiments across environments with diverse global and local dynamics shif...
