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Using distributed W-learning for multi-policy optimization in decentralized autonomic systems

Date
2009
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 de-centralized 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.
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Description
non-peer-reviewed
Publisher
Association for Computing Machinery
Citation
The 6th International Conference on Autonomic Computing and Communications
Collections
Lero - The Science Foundation Ireland Research Centre for Software
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dusparic%203.pdf
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Keywords
distributed w-learning
ULRR Identifiers
https://hdl.handle.net/10344/581
 https://doi.org/10.34961/5375
Funding code
Funding Information
Science Foundation Ireland (SFI)
Sustainable Development Goals
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