Offline Reinforcement Learning for Warehouse SLAM Throughput Control

The article presents an offline reinforcement learning framework aimed at optimizing SLAM (Scan/Label/Apply/Manifest) throughput control in warehouse environments. Key technical details include the use of a history-informed state representation, action space abstraction for delayed-impact control, and a reward function that incorporates both upstream and downstream metrics. The framework integrates multiple offline RL algorithms, with empirical results showing that the CQL policy improves system health by 22.97% and reduces average throttling duration by 3.18%, highlighting the effectiveness of offline RL in enhancing operational efficiency in warehouse settings.