NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 26·5 min read
Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning
TL;DR
Existing offline reinforcement learning methods struggle to adapt to new target environments due to limited data and mismatched dynamics. CEDGE, a Cross-domain Energy-guided Diffusion GEneration framework, generates trajectory-level samples by minimizing distribution mismatch between source and target domains.
✦ Why It Matters
Engineers can leverage CEDGE to improve policy learning in environments with limited data and dynamic changes.
Key Takeaways
- Utilize CEDGE for better adaptation in offline reinforcement learning.
- Implement trajectory-level generation to enhance data coverage.
- Focus on energy guidance to minimize distribution mismatch.
- Test CEDGE on dynamic shifts to evaluate performance improvements.
Full Summary
Offline reinforcement learning (RL) aims to develop policies using pre-collected data, but often faces challenges when the source and target environments have different dynamics. Traditional methods, like reward augmentation, are limited as they cannot create new behaviors beyond existing data.
CEDGE addresses this by employing a trajectory diffusion model that generates new samples based on source-domain trajectories, adapting them to the target domain through energy guidance. This energy guidance minimizes the differences between the source and target trajectories across three components: return, domain, and behavior energy.
Experimental results on the ODRL benchmark indicate that CEDGE significantly improves planning under dynamic shifts and provides synthetic data that enhances target policy learning. The framework's efficiency in adapting to new dynamics without retraining the model marks a notable advancement in offline RL methodologies.