Technical Note: \cal Q-Learning
Machine Learning
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning to solve Markovian decision processes
Learning to solve Markovian decision processes
Multi-agent reinforcement learning in Markov games
Multi-agent reinforcement learning in Markov games
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Readings in agents
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Introduction to Monte Carlo methods
Learning in graphical models
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiagent Coordination with Learning Classifier Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
A Conflict Resolution-Based Decentralized Multi-Agent Problem Solving Model
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Function approximation based multi-agent reinforcement learning
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
Multimedia presentation organization and playout management using intelligent agents
Multimedia Tools and Applications
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Action coordination in multiagent systemsis a difficult task especially in dynamicenvironments. If the environment possessescooperation, least communication,incompatibility and local informationconstraints, the task becomes even moredifficult. Learning compatible action sequencesto achieve a designated goal under theseconstraints is studied in this work. Two newmultiagent learning algorithms called QACE andNoCommQACE are developed. To improve theperformance of the QACE and NoCommQACEalgorithms four heuristics, stateiteration, means-ends analysis, decreasing reward and do-nothing, aredeveloped. The proposed algorithms are testedon the blocks world domain and the performanceresults are reported.