📄 中文摘要
扩散模型在离线强化学习（RL）中的轨迹生成能力表现出色。然而，传统的基于扩散的规划方法往往未能考虑到在RL中生成轨迹需要在过渡之间保持独特的一致性，以确保在真实环境中的连贯性。这一忽视可能导致生成的轨迹与真实环境的基本机制之间存在显著差异。为了解决这一问题，提出了一种新颖的基于扩散的规划方法，称为环境机制建模的扩散调制（DMEMM）。DMEMM通过结合关键的RL环境机制，特别是过渡机制，来调节扩散模型的训练，从而提高轨迹生成的准确性和一致性。
通过环境机制建模的扩散调制用于规划
出处: Diffusion Modulation via Environment Mechanism Modeling for Planning
发布: 2026年2月25日
📄 English Summary
Diffusion Modulation via Environment Mechanism Modeling for Planning
Diffusion models have demonstrated significant capabilities in trajectory generation for offline reinforcement learning (RL). However, conventional diffusion-based planning methods often overlook the necessity of maintaining unique consistency between transitions when generating trajectories in RL, which is crucial for coherence in real environments. This oversight can lead to substantial discrepancies between the generated trajectories and the underlying mechanisms of real environments. To address this issue, a novel diffusion-based planning method, termed Diffusion Modulation via Environment Mechanism Modeling (DMEMM), is proposed. DMEMM enhances the training of diffusion models by incorporating key RL environment mechanisms, particularly focusing on transition dynamics, thereby improving the accuracy and consistency of trajectory generation.