PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.
Multi-agent deep reinforcement learning: a survey.Artificial Intelligence Review, 55(2):895–943
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Smart Commander applies hierarchical reinforcement learning to optimize sequential maintenance, sortie generation, and resource allocation decisions across a military aircraft fleet, outperforming flat DRL and rule-based methods in a custom simulation.
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PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
PC3D trains decentralized policies to recover and use personalized coordination context from local histories, enabling higher returns than baselines on variable-roster cooperative MARL tasks with both seen and unseen team sizes.
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Smart Commander: A Hierarchical Reinforcement Learning Framework for Fleet-Level PHM Decision Optimization
Smart Commander applies hierarchical reinforcement learning to optimize sequential maintenance, sortie generation, and resource allocation decisions across a military aircraft fleet, outperforming flat DRL and rule-based methods in a custom simulation.