AI RESEARCH
Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
arXiv CS.LG
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ArXi:2605.26078v1 Announce Type: new Wasserstein policy gradient (WPG) is a policy optimization method for reinforcement learning (RL) that exploits the optimal-transport geometry of action distributions. For the entropy-regularized RL objective, WPG evolves each state-conditional policy by transporting it along the action gradient of the soft Q-function together with a Langevin-type diffusion. Despite its appeal for continuous-control problems, its global convergence properties remain poorly understood.