The Vision of Autonomic Computing
Computer
Multi-Agent Reinforcement Leraning for Traffic Light Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Multi-Agent Systems Approach to Autonomic Computing
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Requirements for an ubiquitous computing simulation and emulation environment
InterSense '06 Proceedings of the first international conference on Integrated internet ad hoc and sensor networks
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet 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
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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.