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[2512.02435] Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering
Summary
This paper presents a novel framework for cross-domain offline reinforcement learning, introducing a method that filters data based on both dynamics and value alignment to improve agent performance in target environments.
Why It Matters
The research addresses a critical challenge in reinforcement learning where misalignment between source and target domains can lead to poor performance. By emphasizing both dynamics and value alignment, this study provides a more comprehensive approach to data filtering, which could enhance the effectiveness of RL applications in real-world scenarios.
Key Takeaways
- Dynamics alignment alone is insufficient for effective cross-domain RL.
- Value alignment is crucial for selecting high-quality samples from source domains.
- The proposed DVDF method shows significant performance improvements across various tasks.
- Empirical studies demonstrate DVDF's effectiveness in scenarios with limited target domain data.
- The framework can be applied to a range of dynamics shift scenarios.
Computer Science > Machine Learning arXiv:2512.02435 (cs) [Submitted on 2 Dec 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering Authors:Zhongjian Qiao, Rui Yang, Jiafei Lyu, Chenjia Bai, Xiu Li, Siyang Gao, Shuang Qiu View a PDF of the paper titled Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering, by Zhongjian Qiao and 6 other authors View PDF HTML (experimental) Abstract:Cross-domain offline reinforcement learning (RL) aims to train a well-performing agent in the target environment, leveraging both a limited target domain dataset and a source domain dataset with (possibly) sufficient data coverage. Due to the underlying dynamics misalignment between source and target domains, naively merging the two datasets may incur inferior performance. Recent advances address this issue by selectively leveraging source domain samples whose dynamics align well with the target domain. However, our work demonstrates that dynamics alignment alone is insufficient, by examining the limitations of prior frameworks and deriving a new target domain sub-optimality bound for the policy learned on the source domain. More importantly, our theory underscores an additional need for \textit{value alignment}, i.e., selecting high-quality, high-value samples from the source domain, a critical dimension overlooked by existing works. Motiva...
