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Optimal Communication and Control Strategies for a Multi-Agent System in the Presence of an Adversary
We consider a multi-agent system in which a decentralized team of agents controls a stochastic system in the presence of an adversary.
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
The regret bound of the algorithm was derived under a technical assumption on the induced norm of the closed loop system.
Scalable regret for learning to control network-coupled subsystems with unknown dynamics
We consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems connected over a network.
Thompson sampling for linear quadratic mean-field teams
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i. e., empirical mean) of the states and controls.
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