  Training autonomous agents with sparse rewards is a long-standing problem in
online reinforcement learning (RL), due to low data efficiency. Prior work
overcomes this challenge by extracting useful knowledge from offline data,
often accomplished through the learning of action distribution from offline
data and utilizing the learned distribution to facilitate online RL. However,
since the offline data are given and fixed, the extracted knowledge is
inherently limited, making it difficult to generalize to new tasks. We propose
a novel approach that leverages offline data to learn a generative diffusion
model, coined as Adaptive Trajectory Diffuser (ATraDiff). This model generates
synthetic trajectories, serving as a form of data augmentation and consequently
enhancing the performance of online RL methods. The key strength of our
diffuser lies in its adaptability, allowing it to effectively handle varying
trajectory lengths and mitigate distribution shifts between online and offline
data. Because of its simplicity, ATraDiff seamlessly integrates with a wide
spectrum of RL methods. Empirical evaluation shows that ATraDiff consistently
achieves state-of-the-art performance across a variety of environments, with
particularly pronounced improvements in complicated settings. Our code and demo
video are available at https://atradiff.github.io .
