Offline Reinforcement Learning for Warehouse SLAM Throughput Control
On the map: Offline RL and imitation learning from data → Offline RL algorithms
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 16 references that are in the map. First posted on arXiv 22 Jun 2026.
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14 of 16 references.
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- 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun
- 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun
- 2020MOReL : Model-Based Offline Reinforcement Learning12 May
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems4 May
- 2020Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning19 Feb
- 2019Behavior Regularized Offline Reinforcement Learning26 Nov
Other references · 8
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
- 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 840
- 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 494
- 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 30
- 2020Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learningcited by 5
- 2019Manufacturing Dispatching using Reinforcement and Transfer Learningcited by 4
- 2022Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Managementcited by 2
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- 2024A Survey on Reinforcement Learning Applications in SLAM
- 2024Streetwise Agents: Empowering Offline RL Policies to Outsmart Exogenous Stochastic Disturbances in RTC
- 2021Model-Based Offline Planning with Trajectory Pruning
- 2020Offline Reinforcement Learning from Images with Latent Space Models
- 2025Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
- 2026Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization
- 2025Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
- 2023Deploying Offline Reinforcement Learning with Human Feedback