Xuemin Hu

Enhancing Diffusion Policies with Distribution-Matching Generator in Offline Reinforcement Learning

Offline reinforcement learning (RL) can learn policies from pre-collected offline datasets without interacting with the environment, but it suffers from the issue of out-of-distribution (OOD). Recent methods use the generative adversarial paradigm to learn policies, but easily fail to handle the con