Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Optimizing information exchange in cooperative multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Learning to win by reading manuals in a Monte-Carlo framework
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Reinforcement Learning of Communication in a Multi-agent Context
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Cooperative, hybrid agent architecture for real-time traffic signal control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions. Reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead substantially improves the quality of the learned behavior compared to agents not using shout-ahead.