Summary of Diffusion Models As Optimizers For Efficient Planning in Offline Rl, by Renming Huang et al.
Diffusion Models as Optimizers for Efficient Planning in Offline RL
by Renming Huang, Yunqiang Pei, Guoqing Wang, Yangming Zhang, Yang Yang, Peng Wang, Hengtao Shen
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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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 The proposed Trajectory Diffuser addresses the limitations of diffusion models in offline reinforcement learning tasks by decomposing the sampling process into two decoupled subprocesses. The model uses a faster autoregressive approach to generate feasible trajectories while retaining the trajectory optimization process, enabling efficient planning without sacrificing capability. The Trajectory Diffuser is evaluated on the D4RL benchmarks and achieves 3-10 times faster inference speed compared to previous sequence modeling methods, while outperforming them in overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Trajectory Diffuser helps with making decisions by breaking down the process into two parts: creating a good plan and making that plan better. This makes it faster and more efficient without losing its ability to make good choices. The model is tested on certain tasks and performs well, beating previous methods in both speed and quality. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Inference » Optimization » Reinforcement learning
