Contextual Reasoning for Embodied Supply Chain Agents: Reinforcement Learning Policies from Physical State Perception to Collaborative Execution
Main article
Abstract
Physical supply chains increasingly rely on artificial intelligence, autonomous mobile robots, computer vision, edge sensors, and digital twins, yet many decision systems still reason over abstract data tables rather than over the physical state in which execution takes place. This paper develops a contextual reasoning framework for embodied supply chain agents that connects physical state perception, reinforcement learning policy design, and collaborative execution across warehousing, sorting, and last-mile delivery. The proposed framework defines the agent state as a multimodal representation of spatial congestion, shelf load, equipment utilization, order urgency, task risk, and inter-agent dependency. A reward architecture is then formulated to balance fulfillment time, execution accuracy, resource utilization, safety, and policy stability. To demonstrate analytic value, the study constructs an illustrative multi-agent simulation of a three-link supply chain operation involving storage robots, sorting arms, and delivery vehicles. Compared with static rule dispatching, collaborative contextual reinforcement learning reduces average fulfillment time by 30.7%, late-order rate by 51.1%, and near-miss events by 62.1% under the stated scenario assumptions. The analysis shows that contextual reasoning improves not merely prediction accuracy but also the coupling between digital decisions and physical execution. The contribution of the paper is a policy-oriented analytics model that translates embodied supply chain intelligence into implementable reinforcement learning structures, evaluation indicators, and deployment guidelines for AI-enabled adaptive operations.
