Diffusion Modulation via Environment Mechanism Modeling for Planning

TL;DR

This paper introduces DMEMM, a diffusion-based planning method that incorporates environment mechanisms like transition dynamics and rewards, improving trajectory coherence and achieving state-of-the-art results in offline RL planning.

Contribution

The paper proposes a novel diffusion modulation approach that models environment mechanisms to enhance trajectory generation in offline RL planning.

Findings

DMEMM outperforms existing methods in offline RL planning tasks.

Incorporating environment mechanisms improves trajectory coherence.

DMEMM achieves state-of-the-art performance in experiments.

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.