Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
Summary: arXiv:2604.03605v1 Announce Type: cross
Abstract
In the rapidly advancing field of robotics and artificial intelligence, the allocation of tasks among multiple robots remains a significant challenge, especially in environments characterized by asymmetric stochastic arrivals and switching delays. The study presented in this research paper focuses on online task allocation within multi-robot, multi-queue systems.
Problem Formulation
The authors formulate the problem in discrete time, where each location can accommodate at most one robot per time slot. Servicing a task consumes one slot, while switching between locations incurs a one-slot travel delay. Moreover, arrivals at these locations follow independent Bernoulli processes with heterogeneous rates.
Methodology
Building upon prior research highlighting that optimal policies are of an exhaustive type, the authors introduce a discounted-cost Markov decision process. They develop an exhaustive-assignment actor-critic policy architecture that not only enforces exhaustive service by construction but also focuses on learning the next-queue allocation for idle robots.
Comparative Analysis
The proposed policy diverges from the exhaustive-serve-longest (ESL) queue rule, which is known to be optimal only under symmetrical conditions. The novel approach adapts effectively to asymmetric arrival rates, presenting a notable improvement in various scenarios.
Results
Through extensive experimentation across different server-location ratios, loads, and asymmetric arrival profiles, the proposed policy consistently demonstrates:
- Lower discounted holding costs compared to the ESL baseline.
- Smaller mean queue lengths.
- Near-optimal performance in instances where an optimal benchmark is available.
Conclusion
The findings of this study underscore the efficacy of structure-aware actor-critic methods in real-time multi-robot scheduling. By addressing the complexities inherent in asymmetric environments, this research opens new avenues for enhancing the efficiency and effectiveness of multi-robot systems.
In an era where robotics increasingly intersects with various sectors such as logistics, healthcare, and manufacturing, the advancements presented in this research could pave the way for smarter, more adaptive robotic systems capable of optimizing service delivery in real-world applications.