q-learning

New Robust Q-Learning Algorithm Tackles Mean-Field Control Under Wasserstein Uncertainty

A new robust Q-learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law combines quantization-and-projection with a Wasserstein dual reformulation. The algorithm, detailed in an arXiv preprint by researchers Laurière, Mathieu, Neufeld, Ariel, Park, and Kyunghyun, establishes convergence with finite-time iteration bounds for both synchronous and asynchronous learning. Numerical experiments on systemic risk and epidemic models illustrate its robustness-performance tradeoff and convergence behavior.