Technical Note: \cal Q-Learning
Machine Learning
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Efficient learning equilibrium
Artificial Intelligence
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Lenient learners in cooperative multiagent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Predicting and preventing coordination problems in cooperative Q-learning systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Social instruments for robust convention emergence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
The Knowledge Engineering Review
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Coordination in cooperative multiagent systems is an important problem in multiagent learning literature. In practical complex environments, the interactions between agents can be sparse, and each agent's interacting partners may change frequently and randomly. To this end, we investigate the multiagent coordination problems in cooperative environments under the social learning framework. We consider a large population of agents where each agent interacts with another agent randomly chosen from the population in each round. Each agent learns its policy through repeated interactions with the rest of agents via social learning. It is not clear a priori if all agents can learn a consistent optimal coordination policy in such a situation. We distinguish two types of learners: individual action learner and joint action learner. The learning performance of both learners are evaluated under a number of challenging cooperative games, and the influence of the information sharing degree on the learning performance is investigated as well.