Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Convergence Problems of General-Sum Multiagent Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning 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
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Multiagent reinforcement learning and self-organization in a network of agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Real World Multi-agent Systems: Information Sharing, Coordination and Planning
Logic, Language, and Computation
Sharing in teams of heterogeneous, collaborative learning agents
International Journal of Intelligent Systems
Collaborative agent-based learning with limited data exchange
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Self-organization for coordinating decentralized reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Value-function reinforcement learning in Markov games
Cognitive Systems Research
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In the research of team Markov games, computing the coordinate team dynamically and determining the joint action policy are the main problems. To deal with the first problem, a dynamic team partitioning method is proposed based on a novel coordinate tree frame. We build a coordinate tree with coordinate agent subset and define two breaching weights to represent the weights of an agent to corporate with the agent subset. Each agent chooses the agent subset with a minimum cost as the coordinate team based on coordinate tree. The Q-learning based on belief allocation studies multi-agents joint action policy which helps corporative multi-agents joint action policy to converge to the optimum solution. We perform experiments on multiple simulation environments and compare the proposed algorithm with similar ones. Experimental results show that the proposed algorithms are able to dynamically compute the corporative teams and design the optimum joint action policy for corporative teams.