Summary of Diffusion Actor-critic with Entropy Regulator, by Yinuo Wang et al.
Diffusion Actor-Critic with Entropy Regulator
by Yinuo Wang, Likun Wang, Yuxuan Jiang, Wenjun Zou, Tong Liu, Xujie Song, Wenxuan Wang, Liming Xiao, Jiang Wu, Jingliang Duan, Shengbo Eben Li
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes an online reinforcement learning algorithm called diffusion actor-critic with entropy regulator (DACER). Traditional RL algorithms are limited by their parameterization as diagonal Gaussian distributions, constraining their ability to acquire complex policies. DACER addresses this issue by conceptualizing the reverse process of the diffusion model as a novel policy function and leveraging its capability to fit multimodal distributions. The algorithm estimates the entropy of the diffusion policy using a Gaussian mixture model, which allows for adaptively regulating the variance of added noise applied to action outputs. Experimental results on MuJoCo benchmarks and a multimodal task demonstrate DACER’s state-of-the-art performance in most control tasks while showcasing its strong representational capacity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DACER is a new way to do reinforcement learning that lets it learn more complex policies. Right now, most RL algorithms use a simple way of representing their policy as a diagonal Gaussian distribution. This limits what they can learn. DACER changes this by using the reverse process of a special kind of model called a diffusion model. This lets DACER learn from a wider range of data and make better decisions. The algorithm also finds a way to estimate how “mixed up” its actions are, which helps it balance exploration and exploitation. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Mixture model » Reinforcement learning
