Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
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. Standard Langevin analyses do not directly apply, because the RL objective depends on the policy through the Bellman recursion rather than through a static convex functional, and the Langevin drift is determined by the soft ...
| Authors | Zhaoyu Zhu, Rui Gao, Shuang Li |
|---|---|
| Primary category | cs.LG |
| Published | 25 May 2026 |
| Last updated at source | 8 June 2026 |
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