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math.OC2026
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…
math.OC2025
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 t…