Learning to Coordinate: A Critical Synthesis of Reinforcement Learning Approaches for Autonomous Robotics in Warehouse Automation

Abstract

The rapid acceleration of e-commerce has placed unprecedented demands on warehouse logistics, creating a compelling use case for autonomous mobile robot fleets operating at scale. Reinforcement learning has emerged as a promising paradigm for addressing the coordination challenges inherent in multi-robot warehouse automation, offering potential advantages over traditional optimization methods in dynamic, partially observable environments. Yet despite substantial research investment and promising laboratory demonstrations, the translation of reinforcement learning based approaches to industrial practice remains constrained by unresolved theoretical and engineering challenges. This paper presents a systematic critical synthesis of the literature on reinforcement learning for warehouse robotics, examining 67 peer reviewed studies published between 2021 and 2026 across the intersecting domains of multi-agent path finding, task allocation, and collaborative order fulfillment. The analysis identifies three persistent tensions that structure the field: the tradeoff between centralized coordination and distributed scalability, the challenge of transferring policies trained in simulation to noisy physical environments, and the unresolved relationship between individual robot learning and system level optimization objectives. Our findings indicate that while hierarchical and curriculum based reinforcement learning architectures demonstrate genuine promise for specific subproblems, claims of generalizable superiority over classical methods remain insufficiently supported by evidence from realistic, large scale evaluations. We propose an integrative framework that specifies conditions under which reinforcement learning adds value relative to conventional approaches, with implications for research design and industrial deployment.