arXiv:2605.22376v1 Announce Type: new Abstract: Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain. Existing works typically assess the transferability of source-domain data by measuring its similarity to target-domain transitions, and implicitly perform transition-level selection. Transitions that are considered similar are assigned higher weights or rewards, while dissimilar ones are down-weighted. However, transition-level similarity does not necessarily imply consistency in long-term return
The proliferation of AI and large language models makes efficient and effective data utilization for training critical across various domains, leading to new research into cross-domain learning in RL.
This research enhances the ability of AI systems to learn from diverse, existing datasets, potentially reducing data collection costs and improving performance in new environments.
Existing approaches to transferring learning across different domains in offline reinforcement learning are being refined to focus on long-term implications rather than just immediate similarity.
- · AI/ML Research Institutions
- · Companies with extensive but disparate datasets
- · Robotics
- · Autonomous Systems
- · Developers solely relying on single-domain data
- · Traditional offline RL methods
Improved performance and broader applicability of reinforcement learning policies in real-world scenarios.
Accelerated development of AI agents capable of operating effectively in new, previously unencountered environments with less domain-specific training.
Enhanced AI capabilities leading to the automation of more complex tasks requiring adaptive decision-making across varied conditions.
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Read at arXiv cs.LG