Multi-policy optimization in decentralized autonomic systems

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 tra c 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.