通过环境机制建模进行扩散调制用于规划
Diffusion Modulation via Environment Mechanism Modeling for Planning
AI总结：
通过环境机制建模的扩散调制方法提升了离线强化学习中轨迹生成的准确性与一致性。
摘要：
扩散模型在离线强化学习（RL）中的轨迹生成中展示了有前途的能力。然而，传统基于扩散的规划方法往往未能考虑到在RL中生成轨迹需要转换之间具有独特的连贯性以确保现实环境中的一致性。这种忽视可能导致生成的轨迹与现实环境的底层机制之间存在显著差异。为了解决这个问题，我们提出了一种新的基于扩散的规划方法，称为通过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，特别是转换动态和奖励函数，来调节扩散模型的训练。实验结果表明，DMEMM在离线强化学习中的规划任务中实现了最先进的性能。
英文摘要：
Diffusion models have shown promising capabilities in trajectory generation for planning in offline reinforcement learning (RL). However, conventional diffusion-based planning methods often fail to account for the fact that generating trajectories in RL requires unique consistency between transitions to ensure coherence in real environments. This oversight can result in considerable discrepancies between the generated trajectories and the underlying mechanisms of a real environment. To address this problem, we propose a novel diffusion-based planning method, termed as Diffusion Modulation via Environment Mechanism Modeling (DMEMM). DMEMM modulates diffusion model training by incorporating key RL environment mechanisms, particularly transition dynamics and reward functions. Experimental results demonstrate that DMEMM achieves state-of-the-art performance for planning with offline reinforcement learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
