Diffusion Policies creating a Trust Region for Offline Reinforcement Learning

TL;DR

This paper introduces Diffusion Trusted Q-Learning (DTQL), a novel offline RL method that combines diffusion policies with a trust region approach to improve efficiency and performance without iterative sampling.

Contribution

The paper proposes DTQL, which eliminates iterative denoising in diffusion-based offline RL by using a dual policy and trust region loss, enhancing efficiency and effectiveness.

Findings

DTQL outperforms existing methods on D4RL benchmarks.

DTQL achieves faster training and inference speeds.

DTQL maintains high policy expressiveness and exploration capabilities.

Abstract

Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference. While several recent attempts have tried to accelerate diffusion-QL, the improvement in training and/or inference speed often results in degraded performance. In this paper, we introduce a dual policy approach, Diffusion Trusted Q-Learning (DTQL), which comprises a diffusion policy for pure behavior cloning and a practical one-step policy. We bridge the two polices by a newly introduced diffusion trust region loss. The diffusion policy maintains expressiveness, while the trust region loss directs the one-step policy to explore freely and seek modes within the region defined by the diffusion policy. DTQL eliminates the need for iterative denoising sampling during both training and inference, making it remarkably computationally efficient. We evaluate its effectiveness and algorithmic characteristics against popular Kullback--Leibler divergence-based distillation methods in 2D bandit scenarios and gym tasks. We then show that DTQL could not only outperform other methods on the majority of the D4RL benchmark tasks but also demonstrate efficiency in training and inference speeds. The PyTorch implementation is available at https://github.com/TianyuCodings/Diffusion_Trusted_Q_Learning.