Reinforcement Learning and Stochastic Control in Queues and Networks (ReStoq)

Workshop topic

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.

Official webpage

Please click here to visit the official page of the workshop

Organizing committee

Workshop Organizers

  • Qiaomin Xie, University of Wisconsin-Madison, USA
  • Dileep Kalathil, Texas A&M University, USA

Important dates

  Important Dates
September 19, 2022 Workshop date