Summary of Diffpogan: Diffusion Policies with Generative Adversarial Networks For Offline Reinforcement Learning, by Xuemin Hu et al.
DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning
by Xuemin Hu, Shen Li, Yingfen Xu, Bo Tang, Long Chen
First submitted to arxiv on: 13 Jun 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 The proposed method, Diffusion Policies with Generative Adversarial Networks (DiffPoGAN), addresses the extrapolation error issue in offline reinforcement learning by leveraging generative adversarial networks. The approach employs a diffusion model as the policy generator to produce diverse action distributions and incorporates regularization methods based on maximum likelihood estimation and discriminator outputs to constrain policy exploration and improve policy returns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning can learn optimal policies from pre-collected data without interacting with the environment, but often struggles with the extrapolation error issue. A new method called DiffPoGAN aims to address this challenge by using generative adversarial networks (GANs) to generate diverse action distributions and regularize policy exploration for better policy returns. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Likelihood » Regularization » Reinforcement learning
