Distributed reinforcement learning for a traffic engineering application
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-criteria Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multi-Agent Reinforcement Leraning for Traffic Light Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Multiple-goal reinforcement learning with modular Sarsa(O)
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Emergent consensus in decentralised systems using collaborative reinforcement learning
Self-star Properties in Complex Information Systems
A feedback-based decentralised coordination model for distributed open real-time systems
Journal of Systems and Software
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Self-organizing techniques have successfully been used to optimize software systems, such as optimization of route stability in ad hoc network routing and optimization of the use of storage space or processing power using load balancing. Existing self-organizing techniques typically focus on a single, usually implicitly specijied, system goal and tune systems parameters towards optimally meeting that goal. In this papel; we consider optimization of large-scale multi-agent ubiquitous computing environments, such as urban trafJic control. Applications in this class are typically required to optimize towards multiple goals simultaneously. Additionally, these multiple goals can potentially be conjicting, change over time, and apply to various parts of the system such as a single agent, a group of agents, or the system as a whole. In contrast to existing self-organizing systems in which agents are homogeneous to the extent that they are working towards a common goal, agents in these systems are heterogeneous in that they may have di!ering goals. Thus, existing self-organizing optimization techniques must be extended to deal with multiple goal optimization and the resulting heterogeneity of agents. In this paper we present a research agenda for extending Collaborative Reinforcement Learning (CRL), an existing self-organizing optimization technique, to support multiple policy optimization.