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
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Can good learners always compensate for poor learners?
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
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Distributed Reinforcement Learning for Coordinate Multi-Robot Foraging
Journal of Intelligent and Robotic Systems
Multi-agent learning by distributed feature extraction
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Engineering Applications of Artificial Intelligence
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In todayýs open networking environment, the assumption that the learning agents that join a system are homogeneous is becoming increasingly unrealistic. This makes ef fective coordination particularly dif ficult to learn, especially in the absence of learning agent standards. In this short paper we investigate the problem of learning to coordinate with heterogeneous agents. We show that an agent employing the FMQ algorithm, a recently developed multiagent learning method, has the ability to converge towards the optimal joint action when teamed-up with one or more simple Q-learners. Specifically, we show such convergence in scenarios where simple Q-learners alone are unable to converge towards an optimum.