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This paper is devoted to the numerical resolution of McKean-Vlasov controlproblems via the class of mean-field neural networks introduced in ourcompanion paper [25] in order to learn the solution on the Wasserstein space.We propose several algorithms either based on dynamic programming with controllearning by policy or value iteration, or backward SDE from stochastic maximumprinciple with global or local loss functions. Extensive numerical results ondifferent examples are presented to illustrate the accuracy of each of oureight algorithms. We discuss and compare the pros and cons of all the testedmethods.

Mean-field neural networks-based algorithms for McKean-Vlasov control problems *