| Abstract: |
| We investigate the approximation of Coarse Correlated Equilibrium (CCE)
within the framework of continuous-time mean-field games. In this setting, a
regulator (or a correlation device) recommends strategies that agents have no
unilateral incentive to deviate from. We begin by introducing the concept of optimal
CCE and reformulating the problem using a linear programming approach
to demonstrate existence under weak assumptions.
Then, we focus on the approximation of these equilibria, we propose a novel no-regret
primal-dual learning algorithm and prove its convergence. Finally, we provide
numerical examples to illustrate our results. |
|