Queueing Network Controls via Deep Reinforcement Learning

31 Jul 2020 J. G. Dai Mark Gluzman

Novel advanced policy gradient (APG) methods, such as Trust Region policy optimization and Proximal policy optimization (PPO), have become the dominant reinforcement learning algorithms because of their ease of implementation and good practical performance. A conventional setup for notoriously difficult queueing network control problems is a Markov decision problem (MDP) that has three features: infinite state space, unbounded costs, and long-run average cost objective... (read more)

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Methods used in the Paper

Entropy Regularization
Policy Gradient Methods