31 citations · 119 across the 20 of their papers we have counts for
22 papers
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
Jialun Cao, Fernando Acero, David Šiška +1
Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigoro…
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…
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…
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…
Linear convergence of proximal descent schemes on the Wasserstein space
Razvan-Andrei Lascu, Mateusz B. Majka, David Šiška +1
We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on t…
Logarithmic regret in the ergodic Avellaneda-Stoikov market making model
Jialun Cao, David Šiška, Lukasz Szpruch +1
We analyse the regret arising from learning the price sensitivity parameter of liquidity takers in the ergodic version of the Avellaneda-Stoikov market making model. We show th…