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This paper proposes a fully distributed continuous-time algorithm for computing Generalized Nash Equilibria in multi-agent systems with shared constraints, eliminating the need for multiplier exchange to reduce communication overhead and enhance privacy, validated on a multi-robot placement task.
This paper introduces Pareto UCB1 Gossip and Simulated NSW UCB Gossip for multi-objective multi-agent multi-armed bandits, addressing both learning efficiency and fairness in stochastic environments.