Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.
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Discretization error from regularized Reinforcement Learning to continuous-time stochastic control
Derives quantitative convergence rates for the gap between optimal policies from regularized discrete-time Bellman equations and true optimal controls in underlying continuous-time stochastic problems.