Diffusion Modulation via Environment Mechanism Modeling for Planning
This addresses trajectory coherence issues in offline RL planning, offering a domain-specific improvement for robotics and autonomous systems.
The paper tackles the problem of diffusion-based planning methods generating trajectories inconsistent with real environment mechanisms in offline reinforcement learning, proposing DMEMM which modulates diffusion training by incorporating transition dynamics and reward functions, achieving state-of-the-art performance.
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.