Wasserstein policy gradient converges globally in entropy-regularized RL via Bellman-induced distributional PL geometry and uniform LSI, yielding geometric contraction up to discretization bias.
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Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning
Wasserstein policy gradient converges globally in entropy-regularized RL via Bellman-induced distributional PL geometry and uniform LSI, yielding geometric contraction up to discretization bias.