# Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning

**Authors:** Suzan Ece Ada, Erhan Oztop, Emre Ugur  
**arXiv:** [2307.04726](https://arxiv.org/abs/2307.04726)  
**Date:** 2023-07-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.04726v1/

## Key points

- Offline reinforcement learning (RL) algorithms face challenges in handling distribution shifts and effectively representing policies
- Previous work has used conditional diffusion models to represent multimodal behavior but not specifically designed for out-of-distribution state generalization
- The proposed method incorporates state reconstruction feature learning into diffusion policies to address this limitation
- By incorporating state reconstruction loss, the method enhances the ability of diffusion policies to generalize to out-of-distribution states
- The authors design a 2D Multimodal Contextual Bandit environment to demonstrate and evaluate their proposed model
- The model achieves state-of-the-art results on several D4RL benchmark tasks
- The paper presents a novel approach that improves out-of-distribution generalization in offline RL compared to existing approaches.

## Summary

The paper titled "Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning" introduces a novel method to address the challenges faced by offline reinforcement learning (RL) algorithms in handling distribution shifts and effectively representing policies. Offline RL methods leverage previous experiences to learn better policies than the behavior policy used for experience collection, making them suitable for working with non-expert data and multimodal behavior policies. However, offline RL algorithms lack online interaction during training, which poses difficulties in handling distribution shifts and accurately representing policies. Previous work on offline RL has used conditional diffusion models to obtain expressive policies that can represent multimodal behavior in the dataset. However, these models are not specifically designed to alleviate the out-of-distribution state generalization problem. To overcome this limitation, the authors propose a novel method that incorporates state reconstruction feature learning into diffusion policies. This approach aims to promote more descriptive representation learning of states and alleviate the distribution shift caused by out-of-distribution states. By incorporating state reconstruction loss, the proposed method enhances the ability of diffusion policies to generalize to out-of-distribution states. To demonstrate and evaluate their proposed model, the authors design a 2D Multimodal Contextual Bandit environment. They also assess the performance of their model on several D4RL benchmark tasks and achieve state-of-the-art results. In summary, this paper presents a novel approach that addresses the out-of-distribution generalization problem in offline RL by incorporating state reconstruction feature learning into diffusion policies. The proposed method demonstrates improved performance in both a new environment and several benchmark tasks compared to existing approaches.