AINeutralarXiv – CS AI · Jun 26/10
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Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Researchers introduce CG-CMARL, a framework combining coordination graphs with Lagrangian duality to solve constrained multi-agent reinforcement learning problems. The approach decomposes complex joint action spaces into manageable pairwise regions, enabling scalability to larger agent teams while maintaining convergence guarantees and allowing dynamic Pareto front tracing without retraining.