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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2511.17653 |
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| _version_ | 1866911279908651008 |
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| author | Taghavi, Mazyar Vahidi, Javad |
| author_facet | Taghavi, Mazyar Vahidi, Javad |
| contents | Multi-Agent Reinforcement Learning (MARL) has emerged as a powerfulparadigm for cooperative decision-making in connected autonomous vehicles(CAVs); however, existing approaches often fail to guarantee stability, optimality,and interpretability in systems characterized by nonlinear dynamics,partial observability, and complex inter-agent coupling. This study addressesthese foundational challenges by introducing MARL-CC, a unified MathematicalFramework for Multi-Agent Reinforcement Learning with Control Coordination.The proposed framework integrates differential geometric control, Bayesian inference,and Shapley-value-based credit assignment within a coherent optimizationarchitecture, ensuring bounded policy updates, decentralized belief estimation,and equitable reward distribution. Theoretical analyses establish convergence andstability guarantees under stochastic disturbances and communication delays.Empirical evaluations across simulation and real-world testbeds demonstrate upto a 40% improvement in convergence rate and enhanced cooperative efficiencyover leading baselines, including PPO, DDPG, and QMIX.These results signify a decisive advance in control-oriented reinforcement learning,bridging the gap between mathematical rigor and practical autonomy.The MARL-CC framework provides a scalable foundation for intelligent transportation,UAV coordination, and distributed robotics, paving the way toward interpretable, safe, and adaptive multi-agent systems. All codes and experimentalconfigurations are publicly available on GitHub to support reproducibilityand future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MARL-CC: A Mathematical Framework forMulti-Agent Reinforcement Learning in ConnectedAutonomous Vehicles: Addressing Nonlinearity,Partial Observability, and Credit Assignment forOptimal Control Taghavi, Mazyar Vahidi, Javad General Mathematics Multi-Agent Reinforcement Learning (MARL) has emerged as a powerfulparadigm for cooperative decision-making in connected autonomous vehicles(CAVs); however, existing approaches often fail to guarantee stability, optimality,and interpretability in systems characterized by nonlinear dynamics,partial observability, and complex inter-agent coupling. This study addressesthese foundational challenges by introducing MARL-CC, a unified MathematicalFramework for Multi-Agent Reinforcement Learning with Control Coordination.The proposed framework integrates differential geometric control, Bayesian inference,and Shapley-value-based credit assignment within a coherent optimizationarchitecture, ensuring bounded policy updates, decentralized belief estimation,and equitable reward distribution. Theoretical analyses establish convergence andstability guarantees under stochastic disturbances and communication delays.Empirical evaluations across simulation and real-world testbeds demonstrate upto a 40% improvement in convergence rate and enhanced cooperative efficiencyover leading baselines, including PPO, DDPG, and QMIX.These results signify a decisive advance in control-oriented reinforcement learning,bridging the gap between mathematical rigor and practical autonomy.The MARL-CC framework provides a scalable foundation for intelligent transportation,UAV coordination, and distributed robotics, paving the way toward interpretable, safe, and adaptive multi-agent systems. All codes and experimentalconfigurations are publicly available on GitHub to support reproducibilityand future research. |
| title | MARL-CC: A Mathematical Framework forMulti-Agent Reinforcement Learning in ConnectedAutonomous Vehicles: Addressing Nonlinearity,Partial Observability, and Credit Assignment forOptimal Control |
| topic | General Mathematics |
| url | https://arxiv.org/abs/2511.17653 |