SS-MARL combines graph-neural-network message passing with a trust-region constrained update and a new multi-constraint recovery step, reporting better reward and safety trade-offs than MACPO, MAPPO, and InforMARL in cooperative navigation with up to 96 agents.
Constrained policy optimization
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Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System
SS-MARL combines graph-neural-network message passing with a trust-region constrained update and a new multi-constraint recovery step, reporting better reward and safety trade-offs than MACPO, MAPPO, and InforMARL in cooperative navigation with up to 96 agents.