5 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…
Convergence of an actor-critic gradient flow for entropy regularised MDPs in general spaces
Denis Zorba, David Šiška, Lukasz Szpruch
We prove the stability and global convergence of a coupled actor-critic gradient flow for infinite-horizon and entropy-regularised Markov decision processes (MDPs) in continuous st…
Mirror descent actor-critic methods for entropy regularised MDPs in general spaces: stability and convergence
Denis Zorba, David Šiška, Lukasz Szpruch
We provide theoretical guarantees for convergence of discrete-time policy mirror descent with inexact advantage functions updated using temporal difference (TD) learning for entrop…
Mirror descent for constrained stochastic control problems
Deven Sethi, David Šiška
Mirror descent is a well established tool for solving convex optimization problems with convex constraints. This article introduces continuous-time mirror descent dynamics for appr…
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…