1203 Multi - Policy Optimization in Decentralized Autonomic Systems ( Extended
نویسندگان
چکیده
This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban traffic control simulation, a canonical example of a decentralized autonomic system. Our results indicate that W-learning, which learns separately for each policy and then selects between nominated actions based on current action importance, is a suitable approach for optimization towards multiple policies on non-collaborating agents in heterogeneous autonomic environments.
منابع مشابه
Multi-policy optimization in decentralized autonomic systems
Autonomic computing systems are those that are capable of managing themselves based only on highlevel objectives given by humans. In such systems the details of how to meet their objectives, even in the face of changing operating conditions, are left to the systems themselves. Therefore, autonomic systems are required to be able to self-optimize, self-heal, self-protect, and self-configure. Ena...
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