Summary of Diffusion-dice: In-sample Diffusion Guidance For Offline Reinforcement Learning, by Liyuan Mao et al.
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
by Liyuan Mao, Haoran Xu, Xianyuan Zhan, Weinan Zhang, Amy Zhang
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research proposes a novel approach, Diffusion-DICE, which transforms the behavior distribution to the optimal policy distribution using diffusion models. The optimal policy’s score function is decomposed into two terms: the behavior policy’s score function and the gradient of a guidance term that depends on the optimal distribution ratio. The first term can be obtained from a diffusion model trained on the dataset, while the second term is learned through an in-sample learning objective. Due to multi-modality in the optimal policy distribution, the transformation in Diffusion-DICE may guide towards local-optimal modes, and a candidate action selection mechanism is introduced to approach global-optimum. The paper compares Diffusion-DICE with previous diffusion-based offline RL methods on benchmark datasets, demonstrating its strong performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to make better decisions by using “diffusion models” that transform one thing into another. It finds the best solution by breaking it down into two parts: what’s already good and what’s needed to get even better. The first part is learned from data, while the second part is figured out through a special learning process. Because there can be multiple good solutions, this new approach helps guide towards the very best one. It compares its results with other methods and shows it works really well. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
