Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets
arXiv:2605.24862v1 Announce Type: new Abstract: Cross-domain offline reinforcement learning (RL) aims to learn a policy in the target domain with a limited target domain dataset and a source domain dataset that exhibits a dynamics shift. Training directly on the original source dataset typically leads to performance collapse. Recent studies perform data filtering from the perspective of dynamics alignment or value alignment to enable efficient policy transfer. However, these studies are typically validated on single-domain or single-behavior-policy source datasets. In this work, we explore a m
This research addresses a fundamental challenge in applying reinforcement learning across varied datasets, indicating a maturation of the field towards more robust and flexible AI systems.
Improving cross-domain offline reinforcement learning allows AI to learn from a broader range of real-world data, accelerating deployment in complex environments without costly online experimentation.
The ability to unify value alignment and assignment across heterogeneous datasets significantly enhances the practical applicability of RL, moving beyond idealized single-domain training scenarios.
- · AI developers
- · Robotics industry
- · Autonomous systems
- · Data scientists
- · Siloed domain-specific AI approaches
- · High-cost online data collection
- · Trial-and-error RL deployments
More efficient policy learning for AI agents across diverse data sources will become possible.
This could lead to faster and more cost-effective development and deployment of AI in various industries, including manufacturing and autonomous vehicles.
The reduced need for domain-specific data and online interaction might democratize advanced RL applications, making AI agents more ubiquitous.
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