fuzzy reinforcement learning agent for quality of service routing.
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fuzzy reinforcement learning agent for quality of service routing. by Adwait Kulkarni

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Published .
Written in English


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This thesis applies Reinforcement Learning (RL) and Fuzzy Logic to the problem of creating a robust routing algorithm for potential application in a Quality of Service environment with multiple classes of traffic.A value based RL scheme was developed that was a similar to one previously developed by Littman and Boyan [7], but whereas the latter utilized packet hop counts as a reward measure, the RL scheme employed throughout this document uses delay as the primary reward metric. Alternatively, any aggregated QOS measure can be substituted in place of delay, to suit the needs of the particular application environment. This modified scheme was applied in a 10 node network environment that generated packet traffic based on statistics collected from UTORLINK, which monitors packet activity on the back bone of the University of Toronto"s networks. This thesis examined the theoretical "best" and "worst" topological cases for any N node network, and used these formulas to calculate long term expected hop counts.

The Physical Object
Pagination132 leaves.
Number of Pages132
ID Numbers
Open LibraryOL19215732M
ISBN 100494025247

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Another survey paper [73] presents a detailed survey of applications of reinforcement learning to routing in distributed wireless networks is presented in [73]. The interested readers are referred. A Reinforcement Learning-Based Architecture for Fuzzy Logic Control Hamid R. Berenji Artificial Intelligence Research Branch, NASA Ames Research Center, Moffett Field, California ABSTRACT This paper introduces a new method for learning to refine a rule-based fuzzy logic happylifekennel.com by: service-level agreements (SLAs) are two critical factors of dy-namic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic . Reinforcement Learning for Adaptive Routing Leonid Peshkin ([email protected]) Virginia Savova ([email protected]) MIT Artificial Intelligence Lab. Johns Hopkins University Cambridge, MA Baltimore, MD Abstract - Reinforcement learning means learning a policy—a mapping of observations into actions— based on feedback from the Author: Leonid Peshkin, Virginia Savova.

The static neuro-fuzzy agents are used for training and learning to optimize the input and output fuzzy membership functions according to user requirement, and Q-learning (reinforcement learning) static agent is employed for fuzzy inference instead of experts experience. Mobile agents are used to maintain and repair the happylifekennel.com by: 3. A fuzzy reinforcement learning approach for cell outage compensation in radio access networks the required quality of service of a macrocell user can be maintained via the proper selection of. Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks 2. Related Work J. Dowling et al. [2] implemented and analyzed a reinforcement learning algorithm SAMPLE on IEEE They considered the Random. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions. In this paper we show how the reinforcement learning technique can be used to tune the conclusion part of a fuzzy inference system.

reinforcement learning problems. Keywords— Reinforcement learning, Neuro-fuzzy system I. INTRODUCTION Reinforcement learning (RL) paradigm is a computationally simple and direct approach to the adaptive optimal control of nonlinear systems [1]. In RL, the learning agent (controller) interacts with an initially unknown. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in happylifekennel.com: Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabas Poczos, Kenji Shimada, Levent Burak Kara. Reinforcement Learning in Generating Fuzzy Systems Eligibility traces In order to speed up learning, eligibility traces are used to memorize previously visited stateaction pairs, weighted by their proximity at time step t [6, 7]. The trace value indicates how state-action pairs are eligible for learning. Thus, it permits not only tuning of. Feb 10,  · Reinforcement Learning: An Introduction lives up to its name. It is a complete introduction to Reinforcement Learning, which is also known as RL. The book is .