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Found 4 papers, 0 papers with code
Projected gradient-tracking in multi-cluster games and its application to power management
We are concerned with a distributed approach to solve multi-cluster games arising in multi-agent systems.
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games
While the agents inside each cluster collaborate to achieve a common goal, the clusters are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function.
Bandit Learning in Convex Non-Strictly Monotone Games
We address learning Nash equilibria in convex games under the payoff information setting.
Optimization and Control
Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of a static optimal action had she known the sequence of cost functions a priori.
