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• W3100293451 abstract "With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are often unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieve desirable network performance (e.g., high throughput or low delay). In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. To overcome this difficulty, we propose a new algorithm, called RL for Queueing Networks (RL-QN), which applies model-based RL methods over a finite subset of the state space while applying a known stabilizing policy for the rest of the states. We establish that the average queue backlog under RL-QN with an appropriately constructed subset can be arbitrarily close to the optimal result. We evaluate RL-QN in dynamic server allocation, routing, and switching problems. Simulation results show that RL-QN minimizes the average queue backlog effectively." @default.
• W3100293451 created "2020-11-23" @default.
• W3100293451 creator A5008882694 @default.
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• W3100293451 date "2022-03-31" @default.
• W3100293451 modified "2023-09-26" @default.
• W3100293451 title "RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems" @default.
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• W3100293451 doi "https://doi.org/10.1145/3529375" @default.
• W3100293451 hasPublicationYear "2022" @default.
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