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Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers

arXiv 2602.24182 ↗First posted 27 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

Optimizing the consolidation process in container-based fulfillment centers requires trading off competing objectives such as processing speed, resource usage, and space utilization while adhering to a range of real-world operational constraints. This process involves moving items between containers via a combination of human and robotic workstations to free up space for inbound inventory and increase container utilization. We formulate this problem as a large-scale Multi-Objective Reinforcement Learning (MORL) task with high-dimensional state spaces and dynamic system behavior. Our method builds on recent theoretical advances in solving constrained RL problems via best-response and no-regret dynamics in zero-sum games, enabling principled minimax policy learning. Policy evaluation on realistic warehouse simulations shows that our approach effectively trades off objectives, and we empirically observe that it learns a single policy that simultaneously satisfies all constraints, even if this is not theoretically guaranteed. We further introduce a theoretical framework to handle the problem of error cancellation, where time-averaged solutions display oscillatory behavior. This method returns a single iterate whose Lagrangian value is close to the minimax value of the game. These results demonstrate the promise of MORL in solving complex, high-impact decision-making problems in large-scale industrial systems.

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References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2018Reward Constrained Policy Optimizationcited by 397
  5. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  6. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
  7. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 166
  8. 2019Constrained Reinforcement Learning Has Zero Duality Gapcited by 144
  9. 2021Reward is enough for convex MDPscited by 82
  10. 2019Reinforcement Learning with Convex Constraintscited by 57
  11. 2021State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewardscited by 37
  12. 2024The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithmcited by 13
3 more
  1. 2025Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spacescited by 5
  2. 2025Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approachcited by 5
  3. 2024Oracle-Efficient Reinforcement Learning for Max Value Ensemblescited by 1

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