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[2602.20422] Diffusion Modulation via Environment Mechanism Modeling for Planning
Summary
The paper presents a novel approach, Diffusion Modulation via Environment Mechanism Modeling (DMEMM), to enhance trajectory generation in offline reinforcement learning by integrating key environmental dynamics and reward functions.
Why It Matters
This research addresses a critical gap in conventional diffusion-based planning methods, which often overlook the need for consistency in trajectory generation. By incorporating environment mechanisms, DMEMM offers a more coherent approach to planning in real-world applications, potentially improving the effectiveness of reinforcement learning strategies.
Key Takeaways
- DMEMM enhances trajectory generation in offline reinforcement learning.
- The method integrates transition dynamics and reward functions for improved coherence.
- Experimental results indicate DMEMM achieves state-of-the-art performance.
- This approach addresses limitations of conventional diffusion-based planning methods.
- The research contributes to the development of more effective AI planning strategies.
Computer Science > Artificial Intelligence arXiv:2602.20422 (cs) [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 (experimental) Abstract: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. 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 t...