Establishes existence and uniqueness for optimal policies in continuous-time entropy-regularized mean-field control with common noise via an integrated q-function, plus explicit Gaussian characterization in the LQ setting.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundations
Establishes existence and uniqueness for optimal policies in continuous-time entropy-regularized mean-field control with common noise via an integrated q-function, plus explicit Gaussian characterization in the LQ setting.
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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.