arXiv:2605.24810v1 Announce Type: new Abstract: Off-dynamics offline reinforcement learning seeks to learn a target-domain policy from a large source dataset and a limited target dataset under mismatched transition dynamics. Existing approaches such as reward augmentation and data filtering are constrained to the source dataset and cannot synthesize new target behavior to improve coverage beyond the collected source trajectories. While recent model-based methods attempt to address this by learning target-aware dynamics, the generated experience is constructed only at the transition level, whic
This paper addresses a critical challenge in reinforcement learning by proposing a novel method to generate new target-domain behaviors for off-dynamics scenarios, which is crucial for real-world AI deployment where data is mismatched or scarce.
Improving domain generalization and data efficiency in reinforcement learning has significant implications for deploying autonomous AI systems in varied and unpredictable real-world environments, accelerating their practical adoption.
The ability to synthesize robust, target-aware experience beyond collected source trajectories through energy-guided diffusion generation could lead to more adaptive and resilient AI, particularly in robotics and other complex dynamic systems.
- · AI/ML researchers
- · Robotics industry
- · Autonomous systems developers
- · Manufacturing sector
- · Companies with less sophisticated RL data generation techniques
- · Platforms requiring extensive real-world data collection for RL
Off-dynamics reinforcement learning systems become more robust and deployable in varied environments.
Reduced need for extensive, domain-specific data collection in challenging or dangerous environments, accelerating adoption of autonomy.
New complex AI agent applications emerge in previously intractable real-world scenarios due to enhanced adaptability and generalized learning capabilities.
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