Researchers Zhang and Guo developed DMEMM, a novel diffusion-based planning method
The method addresses limitations in conventional approaches for trajectory generation in offline reinforcement learning
DMEMM incorporates key RL environment mechanisms into diffusion model training
The method achieves state-of-the-art performance according to experimental results
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Researchers Hanping Zhang and Yuhong Guo introduced a novel diffusion-based planning method called Diffusion Modulation via Environment Mechanism Modeling (DMEMM) in their paper submitted to arXiv on February 23, 2026, addressing limitations in conventional approaches for trajectory generation in offline reinforcement learning. The paper highlights that while diffusion models have shown promise in generating trajectories for planning in offline reinforcement learning, traditional methods often fail to maintain the necessary consistency between transitions required for coherent real-world applications. This inconsistency can lead to significant discrepancies between generated trajectories and actual environment mechanisms. The researchers developed DMEMM to specifically incorporate key reinforcement learning environment mechanisms, particularly transition dynamics and reward functions, into the diffusion model training process. Experimental results presented in the paper demonstrate that DMEMM achieves state-of-the-art performance for planning with offline reinforcement learning, representing a significant advancement in artificial intelligence and machine learning applications requiring coherent trajectory planning in complex environments.
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of ...
--> Computer Science > Artificial Intelligence arXiv:2602.20422 [Submitted on 23 Feb 2026] Title: Diffusion Modulation via Environment Mechanism Modeling for Planning Authors: Hanping Zhang , Yuhong Guo View a PDF of the paper titled Diffusion Modulation via Environment Mechanism Modeling for Planning, by Hanping Zhang and 1 other authors View PDF HTML Abstract: Diffusion models have shown promising capabilities in trajectory generation for planning in offline reinforcement learning . 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 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. Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) Cite as: arXiv:2602.20422 [cs.AI] (or arXiv:2602.20422v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20422 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuhong Guo [ view email ] [v1] Mon, 23 Feb 2026 23:41:22 UTC (149 KB) Full-text links: Access Paper: View a PDF of the paper titled Diffusion Modulation via Environment Mechanism Modeling for Planning, by Hanping Zhang and 1 other authors View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Schola...
