Efficient Regret Minimization in Non-Convex Games
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1433-1441, 2017.
We consider regret minimization in repeated games with non-convex loss functions. Minimizing the standard notion of regret is computationally intractable. Thus, we define a natural notion of regret which permits efficient optimization and generalizes offline guarantees for convergence to an approximate local optimum. We give gradient-based methods that achieve optimal regret, which in turn guarantee convergence to equilibrium in this framework.