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Steering game dynamics towards desired outcomes
To tackle this challenge, in this work we introduce the SIAR-MPC method, combining the recently introduced Side Information Assisted Regression (SIAR) method for system identification with Model Predictive Control (MPC).
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
A wide array of modern machine learning applications - from adversarial models to multi-agent reinforcement learning - can be formulated as non-cooperative games whose Nash equilibria represent the system's desired operational states.
Discovering How Agents Learn Using Few Data
In this work, we propose a theoretical and algorithmic framework for real-time identification of the learning dynamics that govern agent behavior using a short burst of a single system trajectory.
Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs
In the case of Fisher information geometry, we provide a complete picture of the dynamics in an interesting special setting of team competition via invariant function analysis.
