Learning to select a coordination mechanism
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Minimax Fuzzy Q-Learning in Cooperative Multi-agent Systems
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Modular-Fuzzy Cooperation Algorithm for Multi-agent Systems
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
A Learning Algorithm for Buying and Selling Agents in Electronic Marketplaces
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Employing OLAP mining for multiagent reinforcement learning
Design and application of hybrid intelligent systems
Learning when and how to coordinate
Web Intelligence and Agent Systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
N-learning: a reinforcement learning paradigm for multiagent systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Multiagent association rules mining in cooperative learning systems
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Engineering Applications of Artificial Intelligence
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The application of reinforcement learning to multiagent systems has attracted recent attention. In a multiagent environment, whether one agent's action is good or not depends on the other agents' actions. In traditional reinforcement learning methods, which assume stationary environments, it is hard to take account of the other agent's actions, which may change due to learning. In this article, we consider a two-agent cooperation problem, and propose a multiagent reinforcement learning method based on estimation of the other agent's actions. In our learning method, one agent estimates the other agent's action based on the internal model of the other agent. The internal model is acquired by the observation of the other agent's actions. Through experiments, we demonstrate that good cooperative behaviors are achieved by our learning method.