The workshop will include presentations from invited speakers on stochastic control in queueing systems and stochastic networks. Stochastic control in queueing systems and stochastic networks where specific and stylized model information is used, has a rich history with successes including optimal scheduling and resource allocation in wireless networks and computing systems. In recent times and current applications, model-free approaches such as reinforcement learning, are finding greater applicability owing to increased availability of data of real-world systems and also increased computational capability. To have impact on real-world systems, incorporating model-knowledge and learnings into model-free approaches is necessary, but this is a challenging task. This workshop aims to bring together researchers with expertise in reinforcement learning theory and others with expertise in control in queueing systems and stochastic networks, with a goal to initiate discussions to bridge this challenge.
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|September 19, 2022||Workshop date|