Using Distributed W-Learning for Multi-Policy Optimization in Decentralized Autonomic Systems
Citation:
Ivana Dusparic and Vinny Cahill, Using Distributed W-Learning for Multi-Policy Optimization in Decentralized Autonomic Systems, Proceedings of the 6th International Conference on Autonomic Computing and Communications, 6th International Conference on Autonomic Computing and Communications, Barcelona, Spain, ACM, 2009, 63-64Download Item:
Abstract:
Distributed W-Learning (DWL) is a reinforcement learning-based algorithm for multi-policy optimization in agent-based systems. In this poster we propose the use of DWL for decentralized multi-policy optimization in autonomic systems. Using DWL agents learn and exploit the dependencies between the policies that they are implementing, to collaboratively optimize the performance of an autonomic system. Our initial evaluation shows that DWL is a feasible algorithm for multi-policy optimization in decentralized autonomic systems. Our results show that a multi-policy collaborative DWL deployment outperforms individual single policy deployments, as well non-collaborative deployments.
Author's Homepage:
http://people.tcd.ie/duspariihttp://people.tcd.ie/duspari
http://people.tcd.ie/vjcahill
Description:
PUBLISHEDBarcelona, Spain
Author: DUSPARIC, IVANA; CAHILL, VINNY
Other Titles:
Proceedings of the 6th International Conference on Autonomic Computing and Communications6th International Conference on Autonomic Computing and Communications
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ACMType of material:
PosterCollections
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