CG-CMARL decomposes constrained multi-agent RL into pairwise coordination graphs with shared Q-functions, using Max-Sum message passing and a Lagrangian multiplier to coordinate actions and trace Pareto fronts scalably.
C-morl: Multi-objective reinforcement learning through efficient discovery of pareto front.arXiv preprint arXiv:2410.02236,
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Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
CG-CMARL decomposes constrained multi-agent RL into pairwise coordination graphs with shared Q-functions, using Max-Sum message passing and a Lagrangian multiplier to coordinate actions and trace Pareto fronts scalably.