Distributed reinforcement learning for a traffic engineering application
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
A particle swarm model for swarm-based networked sensor systems
Proceedings of the 2002 ACM symposium on Applied computing
Hybrid Genetic Algorithms for Telecommunications Network Back-Up Routeing
BT Technology Journal
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
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Dynamic preferences in multi-criteria reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Messor: load-balancing through a swarm of autonomous agents
AP2PC'02 Proceedings of the 1st international conference on Agents and peer-to-peer computing
Autonomic multi-policy optimization in pervasive systems: Overview and evaluation
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
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Large-scale autonomic systems are required to self-optimize with respect to high-level policies, that can differ in terms of their priority, as well as their spatial and temporal scope. Decentralized multi-agent systems represent one approach to implementing the required self-optimization capabilities. However, the presence of multiple heterogeneous policies leads to heterogeneity of the agents that implement them. In this paper we evaluate the use of Reinforcement Learning techniques to support the self-optimization of heterogeneous agents towards multiple policies in decentralized systems. We evaluate these techniques in an Urban Traffic Control simulation and compare two approaches to supporting multiple policies. Our results suggest that approaches based on W-learning, which learn separately for each policy and then select between nominated actions based on current action importance, perform better than combining policies into a single learning process over a single state space. The results also indicate that explicitly supporting multiple policies simultaneously can improve waiting times over policies dedicated to optimizing for a single vehicle type.