Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning

📰 ArXiv cs.AI

Learn how Wavelet Fourier Diffuser improves reinforcement learning by incorporating frequency-aware diffusion models, enhancing performance in offline RL tasks

advanced Published 3 Jun 2026

Action Steps

Implement Wavelet Fourier Diffuser using PyTorch or TensorFlow to model trajectory sequences in offline RL tasks
Apply frequency-aware diffusion models to existing RL algorithms to enhance performance
Evaluate the impact of frequency-domain features on RL tasks using metrics such as cumulative reward and episode length
Compare the performance of Wavelet Fourier Diffuser with existing diffusion-based RL methods
Integrate Wavelet Fourier Diffuser with other frequency-domain techniques to further improve RL performance

Who Needs to Know This

Researchers and engineers working on reinforcement learning and offline RL tasks can benefit from this paper, as it introduces a novel approach to improve performance by leveraging frequency-domain features

Key Insight

💡 Incorporating frequency-domain features into diffusion models can significantly improve performance in offline reinforcement learning tasks

Full Article

Title: Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning

Abstract:
arXiv:2509.19305v2 Announce Type: replace-cross Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focus on time-domain features while overlooking frequency-domain features, leading to frequency shift and degraded performance according to our observation. In this paper, we investigate the RL problem from a new perspective of the frequency domain. We first observe

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