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
The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.
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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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Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.