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

摘要

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 collabo-ratively 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.