3 papers
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…
q-fin.TR2024
Ergodic optimal liquidations in DeFi
Jialun Cao, David Šiška
We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem. In decentralised…