  We consider multi-agent decision making, where each agent optimizes its cost
function subject to constraints. Agents' actions belong to a compact convex
Euclidean space and the agents' cost functions are coupled. We propose a
distributed payoff-based algorithm to learn Nash equilibria in the game between
agents. Each agent uses only information about its current cost value to
compute its next action. We prove convergence of the proposed algorithm to a
Nash equilibrium in the game leveraging established results on stochastic
processes. The performance of the algorithm is analyzed with a numerical case
study.
