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Diffusion Modulation via Environment Mechanism Modeling for Planning

Hanping Zhang, Yuhong Guo

TL;DR

DMEMM modulates diffusion model training by incorporating key RL environment mechanisms, particularly transition dynamics and reward functions, and achieves state-of-the-art performance for planning with offline reinforcement learning.

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.

Diffusion Modulation via Environment Mechanism Modeling for Planning

TL;DR

DMEMM modulates diffusion model training by incorporating key RL environment mechanisms, particularly transition dynamics and reward functions, and achieves state-of-the-art performance for planning with offline reinforcement learning.

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.
Paper Structure (27 sections, 1 theorem, 19 equations, 1 figure, 3 tables, 2 algorithms)

This paper contains 27 sections, 1 theorem, 19 equations, 1 figure, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Offline Reinforcement Learning
  4. Diffusion Model in Reinforcement Learning
  5. Preliminaries
  6. Planning with Diffusion Model
  7. Trajectory Representation
  8. Trajectory Diffusion
  9. Training
  10. Method
  11. Modulation of Diffusion Training
  12. Transition-based Diffusion Modulation
  13. Reward-based Diffusion Modulation
  14. Reward-Aware Diffusion Loss
  15. Full Modulation Framework
  16. ...and 12 more sections

Key Result

Proposition 1

Given the reverse process encoded by Eq.(eq:reversediff) and Eq.(eqa:mu) in the diffusion model, the output trajectory $\widehat{\boldsymbol{\tau}}^0$ denoised from an intermediate trajectory $\boldsymbol{\tau}^k$ at step $k$ has the following Gaussian distribution:

Figures (1)

  • Figure 1: Hyperparameter sensitivity analysis of the tradeoff parameters for transition-based diffusion modulation loss ($\lambda_{tr}$) and reward-based diffusion modulation loss ($\lambda_{rd}$) on Hopper-Medium-Expert and Walker2D-Medium-Expert environments.

Theorems & Definitions (2)

  • Proposition 1
  • proof