Diffusion Modulation via Environment Mechanism Modeling for Planning

2026-02-23

AI Summary

This work addresses the inconsistency between generated trajectories and real-world dynamics in offline reinforcement learning when using conventional diffusion models, which often neglect the underlying environmental transition mechanisms. To remedy this, the authors propose a novel approach that explicitly models both the environment’s transition dynamics and the reward function, integrating them directly into the diffusion model training process to impose mechanistic constraints on trajectory generation. This mechanism-aware formulation significantly enhances the environmental consistency of synthesized trajectories and improves downstream planning performance. Empirical evaluations across multiple offline reinforcement learning benchmarks demonstrate that the proposed method achieves state-of-the-art results, thereby validating the efficacy and superiority of incorporating explicit dynamic and reward structures into diffusion-based trajectory modeling.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• diffusion models
• offline reinforcement learning
• trajectory generation
• environment consistency
• transition dynamics

Innovation

• diffusion models
• offline reinforcement learning
• environment mechanism modeling
• transition dynamics
• reward function