31 citations · 119 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
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…
Entropy annealing for policy mirror descent in continuous time and space
Deven Sethi, David Šiška, Yufei Zhang
Entropy regularization has been widely used in policy optimization algorithms to enhance exploration and the robustness of the optimal control; however it also introduces an additi…
Mirror Descent for Stochastic Control Problems with Measure-valued Controls
Bekzhan Kerimkulov, David Šiška, Łukasz Szpruch +1
This paper studies the convergence of the mirror descent algorithm for finite horizon stochastic control problems with measure-valued control processes. The control objective invol…
A Fisher-Rao gradient flow for entropy-regularised Markov decision processes in Polish spaces
Bekzhan Kerimkulov, James-Michael Leahy, David Siska +2
We study the global convergence of a Fisher-Rao policy gradient flow for infinite-horizon entropy-regularised Markov decision processes with Polish state and action space. The flow…