Discretization error from regularized Reinforcement Learning to continuous-time stochastic control

This paper establishes a rigorous connection between regularized discrete-time reinforcement learning (RL) and continuous-time stochastic optimal control. Specifically, classical RL algorithms are typically solving a regularized discrete-time Bellman equation. We study the discretization error, namely, the gap between the optimal policy induced by the regularized discrete-time Bellman equation and the true optimal feedback control of the underlying continuous-time stochastic control problem. By deriving quantitative convergence rates for this gap, we provide a rigorous foundation for understanding the stability and implementation of exploratory RL policies in stochastic continuous-time environments.