Technical Note: \cal Q-Learning
Machine Learning
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-Agent Reinforcement Leraning for Traffic Light Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Improving reinforcement learning with context detection
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
A review of the applications of agent technology in traffic and transportation systems
IEEE Transactions on Intelligent Transportation Systems
An automated signalized junction controller that learns strategies from a human expert
Engineering Applications of Artificial Intelligence
Agent-Based management of non urban road meteorological incidents
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Neural Networks for Real-Time Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Cooperative, hybrid agent architecture for real-time traffic signal control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Reinforcement Learning With Function Approximation for Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Holonification of a network of agents based on graph theory
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Engineering Applications of Artificial Intelligence
Learning via human feedback in continuous state and action spaces
Applied Intelligence
A real-time transportation prediction system
Applied Intelligence
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Multi-agent systems are rapidly growing as powerful tools for Intelligent Transportation Systems (ITS). It is desirable that traffic signals control, as a part of ITS, is performed in a distributed model. Therefore agent-based technologies can be efficiently used for traffic signals control. For traffic networks which are composed of multiple intersections, distributed control achieves better results in comparison to centralized methods. Hierarchical structures are useful to decompose the network into multiple sub-networks and provide a mechanism for distributed control of the traffic signals.In this paper, a two-level hierarchical control of traffic signals based on Q-learning is presented. Traffic signal controllers, located at intersections, can be seen as autonomous agents in the first level (at the bottom of the hierarchy) which use Q-learning to learn a control policy. The network is divided into some regions where an agent is assigned to control each region at the second level (top of the hierarchy). Due to the combinational explosion in the number of states and actions, i.e. features, the use of Q-learning is impractical. Therefore, in the top level, tile coding is used as a linear function approximation method.A network composed of 9 intersections arranged in a 3脳3 grid is used for the simulation. Experimental results show that the proposed hierarchical control improves the Q-learning efficiency of the bottom level agents. The impact of the parameters used in tile coding is also analyzed.