CMAIR: Cooperative Multi-Agent Intrinsic Reward Framework for Enhancing Efficiency in Warehouses

In the landscape of automated warehousing, automated guided vehicles (AGVs) play a key role in enhancing operational accuracy and reducing labor costs. Optimizing the movement path of a group of AGVs, especially in time-sensitive environments, is crucial for maintaining efficiency and reducing costs. We describe this problem as timeliness-constrained multi-agent path finding (TC-MAPF). Although current multi-agent path finding (MAPF) approaches primarily focus on maximizing throughput, they typically overlook the critical need for adhering to stringent timeliness constraints associated with transportation tasks. Moreover, these methods depend heavily on carefully designed reward functions that are specific to particular environmental settings. This reliance constrains their flexibility and broad applicability. In this paper, we propose a novel MAPF algorithm to address the TC-MAPF problem, formulating it as a constrained Markov game. In this formulation, timeliness constraints are integrated into the reward function, ensuring that the agents prioritize meeting these critical deadlines during their operations. Additionally, we introduce a cooperative multi-agent intrinsic reward (CMAIR) framework to enhance the adaptability and generalization of the reward mechanisms across various dynamic environments. The CMAIR framework comprehensively considers the positions and actions of all agents, ensuring efficient exploration and policy optimization. We evaluate our method in various simulated warehouse scenarios and demonstrate that it significantly improves throughput, particularly under stringent time constraints compared to several existing MAPF methods.