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
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
Learning against multiple opponents
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
Multiagent learning is not the answer. It is the question
Artificial Intelligence
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Re-routing Agents in an Abstract Traffic Scenario
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
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Despite the recent results on formalizing multiagent reinforcement learning using stochastic games, the exponential increase of the space of joint actions prevents the use of this formalism in systems of many agents. In fact, most of the literature concentrates on repeated games with single state and few joint actions. However, many real-world systems are comprised of a much higher number of agents. Also, these are normally not homogeneous and interact in environments which are highly dynamic. This paper discusses the implications of co-evolution between two classes of agents in stochastic games using learning automata. These agents interact in a urban traffic scenario where approaches based on the standard stochastic games are prohibitive. The approach was tested in a network with different traffic conditions.