• Computer Science > Artificial Intelligence [Submitted on 23 Feb 2026] Title:Diffusion Modulation via Environment Mechanism Modeling for Planning 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.

Article Summaries:

  • Researchers have introduced Diffusion Modulation via Environment Mechanism Modeling (DMEMM), a new diffusion‑based planning approach for offline reinforcement learning (RL). Traditional diffusion models generate trajectories without ensuring that successive transitions remain consistent with the underlying environment dynamics, often producing unrealistic plans. DMEMM addresses this by embedding key RL mechanisms-specifically transition dynamics and reward functions-directly into the diffusion training process. Experiments show that the method outperforms existing offline‑RL planning techniques, achieving state‑of‑the‑art results and demonstrating improved coherence between generated trajectories and real‑world environment behavior.

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