@article {
	title = {Distributed Learning Algorithm for Multi agent Welfare Optimization},
	author = {Menon, Anup., Baras, John S.},
	year = {2016},
	month = {01/2016},
	abstract = {A multi-agent system comprising N non-strategic agents is considered. Each agent picks actions~from a finite set and receives a payoff that depends on the actions of the whole. The setting is non-model~based where the exact form of the utility (or payoff) functions are unknown, and only their ~values can~be measured by the respective agents. A distributed algorithm which enables the agents to learn to play~welfare (i.e. the sum of individual utilities) optimal actions is proposed. While the exact form of the~utilities is unknown, a novel framework is developed to capture implicit communication{\textemdash}the measured~change in utility by an agent due to actions by another{\textemdash}and convergence of the algorithm is proved~under appropriate assumptions on implicit and explicit communications. The proposed algorithm has~kinship with a body of work in learning in repeated games and the main analysis tool is perturbed~Markov chains.},
	journal = {IEEE Transactions on Automatic Control}
}
