Evolutionary learning of communicating agents
Information Sciences—Informatics and Computer Science: An International Journal
Multi-agent Robot Learning by Means of Genetic Programming: Solving an Escape Problem
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Convergence Problems of General-Sum Multiagent Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multi-Agent Reinforcement Learning for Planning and Scheduling Multiple Goals
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
When evolving populations is better than coevolving individuals: the blind mice problem
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Reinforcement learning in distributed domains: beyond team games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Expertness based cooperative Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Some multiagent learning methods simply extend reinforcement learning to multiple agents. In these methods, large state and action spaces are the most difficult problems. Moreover, previous proposals for using learning techniques to coordinate multiple agents have mostly relied on explicit or implicit information sharing, which makes cooperation affected by communication delays and the reliability of the information received. A Multiagent Cooperative Learning Algorithm (MCLA) is presented to solve these problems. In MCLA, an evaluating strategy based on long-time reward is proposed. Thus each agent acts independently and autonomously by perceiving and estimating each other. It also considers the learning process from the holistic point of view to obtain the optimum associated action strategy in order to reduce the state and action spaces. A series of simulations are provided to demonstrate the performance of the proposed algorithm.