mlsanityData to 14 Oct 2026

Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning

arXiv 2604.03605 ↗First posted 4 Apr 2026eess.SY cs.AI cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

We study online task allocation for multi-robot, multi-queue systems with asymmetric stochastic arrivals and switching delays. We formulate the problem in discrete time: each location can host at most one robot per slot, servicing a task consumes one slot, switching between locations incurs a one-slot travel delay, and arrivals at locations are independent Bernoulli processes with heterogeneous rates. Building on our previous structural result that optimal policies are of exhaustive type, we formulate a discounted-cost Markov decision process and develop an exhaustive-assignment actor-critic policy architecture that enforces exhaustive service by construction and learns only the next-queue allocation for idle robots. Unlike the exhaustive-serve-longest (ESL) queue rule, whose optimality is known only under symmetry, the proposed policy adapts to asymmetry in arrival rates. Across different server-location ratios, loads, and asymmetric arrival profiles, the proposed policy consistently achieves lower discounted holding cost and smaller mean queue length than the ESL baseline, while remaining near-optimal on instances where an optimal benchmark is available. These results show that structure-aware actor-critic methods provide an effective approach for real-time multi-robot scheduling.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 19 references that are in the map. First posted on arXiv 4 Apr 2026.

Builds on

7 of 19 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Queueing Network Controls via Deep Reinforcement Learningcited by 41
  3. 2020RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systemscited by 11
  4. 2024Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systemscited by 7
  5. 2023Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stabilitycited by 3
  6. 2025A Novel Switch-Type Policy Network for Resource Allocation Problems: Technical Reportcited by 1
  7. 2025Exhaustive-Serve-Longest Control for Multi-robot Scheduling Systemscited by 1

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  1. 2026Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts
  2. 2025Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays
  3. 2025Data-Efficient Multitask DAgger
  4. 2026Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams
  5. 2024Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks
  6. 2024Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
  7. 2024Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
  8. 2024Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning