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A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks by Wang, Wenbo; Kwasinski, Andres; Niyato, Dusit; Han, Zhu is a scholarly article available to read on EtoBox.

What is A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks about?

Model-free learning has been considered as an efficient tool for designing control mechanisms when the model of the system environment or the interaction between the decision-making entities is not available as a-priori knowledge. With model-free learning, the decision-making entities adapt their behaviors based on the reinforcement from their interaction with the environment and are able to (implicitly) build the understanding of the system through trial-and-error mechanisms. Such characteristics of model-free learning is highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Recently, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models that those applications are based on, a systematic overview of the learning algorithms in the domains of single-agent system, multi-agent systems and multi-player games is provided. Furthermore, the applic

Author

Wang, Wenbo; Kwasinski, Andres; Niyato, Dusit; Han, Zhu

Published

2015

Language

EN