6 papers
Reward-Rate Congestion Games and Replicator--Dinkelbach Dynamics
Hassan Abdelraouf, Vaibhav Srivastava, Vijay Gupta
Reward rate is a key performance criterion in cyber-physical and robotic systems where time, workload, and coordination costs are limiting resources. We introduce reward-rate conge…
Stabilization Limits of Payoff-Based Higher-Order Replicator Dynamics
Hassan Abdelraouf, Vijay Gupta, Jeff S. Shamma
Replicator dynamics (RD) is a fundamental model in learning in games, connecting evolutionary game theory and online learning. This paper studies payoff-based higher-order variants…
Convergence of Payoff-Based Higher-Order Replicator Dynamics in Contractive Games
Hassan Abdelraouf, Vijay Gupta, Jeff S. Shamma
We study the convergence properties of a payoff-based higher-order version of replicator dynamics, a widely studied model in evolutionary dynamics and game-theoretic learning, in c…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
Learning Neural Koopman Operators with Dissipativity Guarantees
Yuezhu Xu, S. Sivaranjani, Vijay Gupta
We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative.…
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju +4
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of co…