Online distributed NE seeking for multi-agent noncooperative games with time-varying delays
A distributed online gradient-descent algorithm is developed to seek the time-varying Nash equilibrium in the game and is proven to achieve a sublinear dynamic regret bound via an appropriate learning rate, provided that the path variations grow sublinearly.