Control Techniques for Complex Networks: Workload

By Sean P. Meyn. 2007

365 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Management Information Systems

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Citations in the corpus

Control variates for estimation based on reversible Markov chain Monte Carlo samplers (2012, methodology): “Also, Meyn (2007), chapter 11, gave a development of the general control variates methodology for Markov chain data that parallels certain parts of our presentation in Section 2 and discussed numerous related asymptotic results and implementational issues.”
Natural actor-critic algorithms (2009, background): “Next, in Lemma 2, we show that if the parameter is set to be equal to w⋆, then the resulting mean squared error Eπ(w⋆) (now treated as a function of the baseline b(s)) is further minimized when b(s) = V π(s) (see also Chapter 11 of [53]).”
Q-learning and enhanced policy iteration in discounted dynamic programming (2010, background): “[23], Meyn [32], Powell [33], Buşoniu et al.”
Network Optimization and Control (2008, methodology): “A stochastic version of the stability proof of [90] is presented in [91] using an extension of Lyapunov’s theorem for Markov chains (see [92], for example).”
Mean-Variance Optimization in Markov Decision Processes (2011, methodology): “ny constraints on the mean cumulative reward? Third, bias-variance tradeoffs may pay an important role inspeeding up certain control and learning heuristics, such as those involving control variates (Meyn, 2008). Perhaps mean-variance optimization can be used to address the exploration/exploitation ”
Prescriptive analytics: Literature review and research challenges (2020, neutral): “MDP is a discrete time stochastic control process which provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker (Meyn, 2008).”