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cs.LG2026
Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs
Ziyue Chen, David Šiška, Lukasz Szpruch
We study the global convergence of policy gradient for infinite-horizon entropy-regularized Markov decision processes (MDPs) with continuous state and action spaces. We consider lo…
cs.LG2026
A note on convergence of Wasserstein policy optimization
David Šiška, Yufei Zhang
Wasserstein Policy Optimization (WPO) is a recently proposed reinforcement learning algorithm that leverages Wasserstein gradient flows to optimize stochastic policies in continuou…
cs.LG2025
PPO in the Fisher-Rao geometry
Razvan-Andrei Lascu, David Šiška, Łukasz Szpruch
Proximal Policy Optimization (PPO) is widely used in reinforcement learning due to its strong empirical performance, yet it lacks formal guarantees for policy improvement and conve…