Summary of Long-horizon Rollout Via Dynamics Diffusion For Offline Reinforcement Learning, by Hanye Zhao et al.
Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
by Hanye Zhao, Xiaoshen Han, Zhengbang Zhu, Minghuan Liu, Yong Yu, Weinan Zhang
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the potential of diffusion models in decision-making and control. By decoupling diffusion models’ ability as dynamics models in fully offline settings, they develop a new approach called Dynamics Diffusion (DyDiff) that can be used to improve policy consistency and rollout accuracy in model-free algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors explain that current methods for using diffusion models directly sample from the trajectory space, which can lead to a mismatch between the behavior policy and the learning policy. DyDiff addresses this issue by iteratively injecting information from the learning policy into the diffusion model, allowing it to learn the data distribution from the dataset. This approach is shown to be effective in offline reinforcement learning settings where the rollout dataset is provided but no online environment for interaction. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Reinforcement learning
