Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
📰 ArXiv cs.AI
Multi-robot multi-queue control is achieved through exhaustive assignment actor-critic learning, handling asymmetric stochastic arrivals and switching delays
Action Steps
- Formulate the problem in discrete time with asymmetric stochastic arrivals and switching delays
- Model the system as a multi-robot, multi-queue system with independent Bernoulli process arrivals
- Apply exhaustive assignment actor-critic learning to optimize task allocation
- Evaluate the performance of the algorithm in handling switching delays and heterogeneous arrival rates
Who Needs to Know This
This research benefits robotics and control systems engineers, as well as AI researchers working on multi-agent systems, who need to optimize task allocation in complex environments
Key Insight
💡 Exhaustive assignment actor-critic learning can effectively optimize task allocation in multi-robot, multi-queue systems with asymmetric stochastic arrivals and switching delays
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🤖💡 Multi-robot multi-queue control via exhaustive assignment actor-critic learning
Key Takeaways
Multi-robot multi-queue control is achieved through exhaustive assignment actor-critic learning, handling asymmetric stochastic arrivals and switching delays
Full Article
Title: Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
Abstract:
arXiv:2604.03605v1 Announce Type: cross Abstract: 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 th
Abstract:
arXiv:2604.03605v1 Announce Type: cross Abstract: 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 th
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