mlsanityData to 7 Oct 2026

Diffusion Modulation via Environment Mechanism Modeling for Planning

arXiv 2602.20422 ↗First posted 23 Feb 2026cs.AI cs.LGcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 20 of its 24 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

20 of 24 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Simple Hierarchical Planning with Diffusion5 Jan
  2. 2023Efficient Planning with Latent Diffusion30 Sep
  3. 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL31 May

Other references · 17

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  8. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  9. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  10. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
  11. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  12. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
5 more
  1. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  2. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  3. 2019Behavior Regularized Offline Reinforcement Learningcited by 654
  4. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  5. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582

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  1. 2024Diffusion Models as Optimizers for Efficient Planning in Offline RL
  2. 2025Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
  3. 2025DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
  4. 2025What Makes a Good Diffusion Planner for Decision Making?
  5. 2024Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
  6. 2025Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation
  7. 2025Prior-Guided Diffusion Planning for Offline Reinforcement Learning
  8. 2026Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning