Using Distributed W-Learning for Multi-Policy Optimization in Decentralized Autonomic Systems

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.