The Mechanics of n-Player Differentiable Games

Understand

The cornerstone underpinning deep learning is the guarantee that gradient descent on an objective converges to local minima.

• Unfortunately, this guarantee fails in settings, such as generative adversarial nets, where there are multiple interacting losses.
• The behavior of gradient-based methods in games is not well understood -- and is becoming increasingly important as adversarial and multi-objective architectures proliferate.
• In this paper, we develop new techniques to understand and control the dynamics in general games.