From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning
Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as diffusion and flow matching enhance policy expressiveness, they typically start from uninformative Gaussian noise and generate actions through multi-step iteration, lea...
Sheng Lu, Zi-Xuan Yang, Rong-Xu Zhao et al.
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