A bisection-based adaptive algorithm converges to a mean field equilibrium for scalar-interaction dynamic games, and a model-free Q-learning variant learns it from simulation.
Specifically, we assume thatM:P(X)→ M ⊂R n, whereMis a compact set
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Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications
A bisection-based adaptive algorithm converges to a mean field equilibrium for scalar-interaction dynamic games, and a model-free Q-learning variant learns it from simulation.