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Using Reinforcement Learning for Multi-policy Optimization in Decentralized Autonomic Systems – An Experimental Evaluation
[chapter]
2009
Lecture Notes in Computer Science
Large-scale autonomic systems are required to self-optimize with respect to high-level policies, that can differ in terms of their priority, as well as their spatial and temporal scope. Decentralized multiagent systems represent one approach to implementing the required selfoptimization capabilities. However, the presence of multiple heterogeneous policies leads to heterogeneity of the agents that implement them. In this paper we evaluate the use of Reinforcement Learning techniques to support
doi:10.1007/978-3-642-02704-8_9
fatcat:cn7f7edoujasnp3lqomizg7k6i