Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games

Understand

Regret minimization is a powerful tool for solving large-scale extensive-form games.

• State-of-the-art methods rely on minimizing regret locally at each decision point.
• In this work we derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points and linear losses.
• We call our framework laminar regret decomposition.