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cs.LG2026
Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
Zhaoyu Zhu, Rui Gao, Shuang Li
Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action distributions. For the entr…
cs.LG2026
Wasserstein Proximal Policy Gradient
Zhaoyu Zhu, Shuhan Zhang, Rui Gao +1
We study policy gradient methods for continuous-action, entropy-regularized reinforcement learning through the lens of Wasserstein geometry. Starting from a Wasserstein proximal up…
cs.LG2025
Elucidating Rectified Flow with Deterministic Sampler: Polynomial Discretization Complexity for Multi and One-step Models
Ruofeng Yang, Zhaoyu Zhu, Bo Jiang +2
Recently, rectified flow (RF)-based models have achieved state-of-the-art performance in many areas for both the multi-step and one-step generation. However, only a few theoretical…