Intelligent Multi-Agent Reinforcement Learning Architectures for Coordinated Autonomous Logistics and Real-Time Network Optimization

The complexity and variability of large-scale global logistics networks demonstrate the inherent limits to the potential of both optimized centralization and automated rules based on the present state of knowledge. Logistics systems today function in decentralized, stochastic and partially observable environments, comprising autonomous, however, dependent upon each other, entities such as trucks, warehouses and transportation hubs. This paper provides an overall theoretical and architectural base for the application of Intelligent Multi-Agent Reinforcement Learning (MARL) as a platform for the development of autonomous logistics and the dynamic optimization of logistics networks in real time. Logistics operations are defined as decentralized decision-making processes and stochastic games, which allow agents to develop adaptive coordination policies, through decentralized execution of policies developed during centralized training. An additional layered MARL structure is described to separate perception, coordination, decision-making and optimization, to ensure the ability to scale, modularize and optimize logistics networks in a stable manner. Graph-based communication, message-passing mechanisms and bandwidth-efficient policy-sharing are used to coordinate the actions among agents; whereas, the stability of learning is addressed using value decomposition, structured credit assignment and reward shaping. Advanced learning strategies including actor-critic methods, proximal policy optimization, meta-learning and continual learning are analyzed for multi-objective optimization of logistics networks over time, cost, energy and carbon footprint constraints. In addition, this paper demonstrates how the proposed framework can be integrated with high-fidelity simulation and multiagent digital twins to safely train and validate policies under realistic disruptions, along with cloud-edge infrastructure and distributed data pipelines to deploy these policies in real time. Additionally, the paper addresses the issues of interoperability between the proposed MARL framework and enterprise supply chain systems, as well as the governance issues related to transparency, accountability and regulatory compliance. Finally, the paper outlines future research directions, combining MARL with graph neural networks, generative models and predictive digital twins to enable scalable, resilient and self-optimizing logistics ecosystems.

Multi-Agent Reinforcement Learning, Autonomous Logistics Systems

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