Efficient Regret Minimization in Non-Convex Games

Elad Hazan; Karan Singh; Cyril Zhang

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arXiv · 1708.00075

Efficient Regret Minimization in Non-Convex Games

Elad Hazan·Karan Singh·Cyril Zhang

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Abstract

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.

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Elad Hazan, Karan Singh, Cyril Zhang. 2017-07-31. Efficient Regret Minimization in Non-Convex Games. https://arxiv.org/abs/1708.00075

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