mlsanityData to 14 Oct 2026

Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping

arXiv 2503.09755 ↗First posted 12 Mar 2025cs.LG cs.ROcited by 2

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Abstract (arXiv)

In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lead to increased package recirculation. To tackle this challenge, we introduce a Distributionally Robust Multi-Agent Reinforcement Learning (DRMARL) framework that learns a destination-to-chute mapping policy that is resilient to adversarial variations in induction rates. Specifically, DRMARL relies on group distributionally robust optimization (DRO) to learn a policy that performs well not only on average but also on each individual subpopulation of induction rates within the group that capture, for example, different seasonality or operation modes of the system. This approach is then combined with a novel contextual bandit-based predictor of the worst-case induction distribution for each state-action pair, significantly reducing the cost of exploration and thereby increasing the learning efficiency and scalability of our framework. Extensive simulations demonstrate that DRMARL achieves robust chute mapping in the presence of varying induction distributions, reducing package recirculation by an average of 80% in the simulation scenario.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 12 Mar 2025.

Builds on

27 of 67 references.

References · 27

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,247
  3. 2019The StarCraft Multi-Agent Challengecited by 597
  4. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  5. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  6. 2017Robust Adversarial Reinforcement Learningcited by 451
  7. 2017A Tutorial on Thompson Samplingcited by 433
  8. 2019Distributionally Robust Language Modelingcited by 143
  9. 2022Robust Reinforcement Learning using Offline Datacited by 96
  10. 2019Distributionally Robust Reinforcement Learningcited by 42
  11. 2024Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithmscited by 26
  12. 2023Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithmscited by 25
15 more
  1. 2024Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertaintycited by 25
  2. 2019A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Networkcited by 22
  3. 2020Robust Reinforcement Learning with Wasserstein Constraintcited by 21
  4. 2023On the Foundation of Distributionally Robust Reinforcement Learningcited by 18
  5. 2021Distributionally Robust Policy Learning via Adversarial Environment Generationcited by 17
  6. 2022Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyondcited by 17
  7. 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
  8. 2024Distributionally Robust Constrained Reinforcement Learning under Strong Dualitycited by 15
  9. 2021A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principlecited by 13
  10. 2022Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variablescited by 7
  11. 2024Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learningcited by 6
  12. 2024Safe and Robust Reinforcement Learning: Principles and Practicecited by 4
  13. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
  14. 2023Distributed robust optimization for multi-agent systems with guaranteed finite-time convergencecited by 1
  15. 2023Data-Driven Distributionally Robust Mitigation of Risk of Cascading Failurescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorizationcited by 0
  2. 2026Stationary Robust Mean-Field Games under Model Mismatchescited by 0

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