Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
This paper is a continuation work of Ren et al. (2026) aiming to further devise q-learning algorithms for mean-field control (MFC) with controlled common noise. Based on the relaxed control formulation, we first establish the martingale condition of the value function and the Iq-function by evaluating along the conditional state distributions generated by all test policies. As the data in the relaxed control formulation are not observable in practice, we quantify the error incurred when they are replaced by the observable ones in the exploratory formulation under discretely sampled actions. This, together with a two-layer fixed point characterization of an optimal policy in Ren et al. (20...
| Authors | Zhenjie Ren, Xiaoli Wei, Xiang Yu et al. |
|---|---|
| Primary category | math.OC |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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