Offline Reinforcement Learning for Warehouse SLAM Throughput Control

• URL: http://arxiv.org/abs/2606.23978v1
• Date: Mon, 22 Jun 2026 22:10:06 GMT
• Title: Offline Reinforcement Learning for Warehouse SLAM Throughput Control
• Abstract summary: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse environment.<n>Our RL-based control approach dynamically recommends SLAM throughput settings that adaptively balance fulfillment-ir with downstream throughput stability.<n>We instantiate our framework with three state-of-the-art offline algorithms, and trained the models offline using historical operational logs from a large-scale warehouse.
• Score: 5.296008945250353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment. SLAM (Scan/Label/Apply/Manifest) throughput directly influences system congestion and operational efficiency. Our RL-based control approach dynamically recommends SLAM throughput settings that adaptively balance throughput maximization with downstream stability through intelligent adjustment of throttling behavior. We include a history-informed state representation, action space abstraction for delayed-impact control, and a reward function that captures both upstream and downstream operational metrics. Our approach is algorithm-agnostic, enabling integration of multiple offline RL methods under a unified architecture. We instantiate our framework with three state-of-the-art offline RL algorithms, and trained the models offline using de-identified historical operational logs from a large-scale warehouse. Policy performance is evaluated using a comprehensive multi-method strategy. These include model-free approaches including immediate reward estimation via regression models and long-horizon Fitted Q Evaluation (FQE), as well as model-based Deep Koopman dynamics evaluation. Empirical results reveal that the CQL policy consistently outperforms alternatives, improving system health by 22.97% and reducing average throttling duration by 3.18%. These findings demonstrate the potential of offline RL for safe and scalable warehouse throughput control optimization.

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