Technical Note: \cal Q-Learning
Machine Learning
The Vision of Autonomic Computing
Computer
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Digital Evolution of Behavioral Models for Autonomic Systems
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Messor: load-balancing through a swarm of autonomous agents
AP2PC'02 Proceedings of the 1st international conference on Agents and peer-to-peer computing
Autonomic policy adaptation using decentralized online clustering
Proceedings of the 7th international conference on Autonomic computing
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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.