Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918427900248064 |
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| author | Merati, Mohammad Ahmad, H. M. Sabbir Li, Wenchao Castañón, David |
| author_facet | Merati, Mohammad Ahmad, H. M. Sabbir Li, Wenchao Castañón, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03605 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning Merati, Mohammad Ahmad, H. M. Sabbir Li, Wenchao Castañón, David Systems and Control Artificial Intelligence Optimization and Control 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. |
| title | Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning |
| topic | Systems and Control Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2604.03605 |