Technical Note: \cal Q-Learning
Machine Learning
On simple algorithms for dynamic load balancing
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 1)-Volume - Volume 1
A Goal-based Approach to Policy Refinement
POLICY '04 Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks
Policy-Based Mobile Ad Hoc Network Management
POLICY '04 Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Quality-of-service mapping mechanism for packet video indifferentiated services network
IEEE Transactions on Multimedia
An automated policy-based management framework for differentiated communication systems
IEEE Journal on Selected Areas in Communications
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This paper presents a policy-based framework to approach the issue of autonomous reconfiguration management in heterogeneous networks. In contrast to existing policy-based approaches, the proposed framework addresses the management issue from a new perspective through posing it as a problem of learning from current network behavior, while creating and updating policies dynamically in response to changing reconfiguration requirements, and this task is implemented by Reinforcement Learning methodology. A two-layer policy model is used to mapping users and operators' higher level goals into network level objectives. The autonomic reconfiguration procedures for policy creation, storage, evaluation are also presented in detail. Illustrative examples analysis and simulation results demonstrate the performance of the proposed work.