Title: QUAD: Q-Gradient Uncertainty-Aware Guidance for Diffusion policies in Offline Reinforcement Learning
Primary area: reinforcement learning
Keywords: Offline RL, Diffusion Policy

Abstract: Diffusion-based offline reinforcement learning (RL) leverages Q-gradients of noisy actions to guide the denoising process. Existing approaches fall into two categories: (i) backpropagating the Q-gradient of the final denoised action through all steps, or (ii) directly estimating the Q-gradient of noisy actions. The former suffers from exploding or vanishing gradients as the number of denoising steps increases, while the latter becomes inaccurate when noisy actions deviate substantially from the dataset. In this work, we focus on addressing the limitations of the second category. We introduce QUAD, an uncertainty-aware Q-gradient guidance method. QUAD employs a Q-ensemble to estimate the uncertainty of Q-gradients and uses this uncertainty to constrain unreliable guidance during denoising. By down-weighting unreliable gradients, QUAD reduces the risk of producing suboptimal actions. Experiments on the D4RL benchmark show that QUAD outperforms state-of-the-art methods across most tasks.

TL;DR: We introduce QUAD, an uncertainty-aware Q-gradient guidance for diffusion-based offline RL. By down-weighting unreliable gradients, QUAD achieves state-of-the-art performance on D4RL benchmarks.