The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
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
A Multiagent System for Optimizing Urban Traffic
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning from Multiple Sources
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Using cooperative mediation to coordinate traffic lights: a case study
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Dealing with non-stationary environments using context detection
ICML '06 Proceedings of the 23rd international conference on Machine learning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
ITSUMO: an Intelligent Transportation System for Urban Mobility
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Traffic lights control with adaptive group formation based on swarm intelligence
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Distributed and adaptive traffic signal control within a realistic traffic simulation
Engineering Applications of Artificial Intelligence
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
Holonic multi-agent system for traffic signals control
Engineering Applications of Artificial Intelligence
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Computer science in general, and artificial intelligence and multiagent systems in particular, are part of an effort to build intelligent transportation systems. An efficient use of the existing infrastructure relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. In particular, traffic signal controllers located at intersections can be seen as autonomous agents. However, challenging issues are involved in this kind of modeling: the number of agents is high; in general agents must be highly adaptive; they must react to changes in the environment at individual level while also causing an unpredictable collective pattern, as they act in a highly coupled environment. Therefore, traffic signal control poses many challenges for standard techniques from multiagent systems such as learning. Despite the progress in multiagent reinforcement learning via formalisms based on stochastic games, these cannot cope with a high number of agents due to the combinatorial explosion in the number of joint actions. One possible way to reduce the complexity of the problem is to have agents organized in groups of limited size so that the number of joint actions is reduced. These groups are then coordinated by another agent, a tutor or supervisor. Thus, this paper investigates the task of multiagent reinforcement learning for control of traffic signals in two situations: agents act individually (individual learners) and agents can be ''tutored'', meaning that another agent with a broader sight will recommend a joint action.