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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
No-commitment branch and bound search for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Decentralized control of adaptive sampling in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Decentralised dynamic task allocation: a practical game: theoretic approach
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
The Knowledge Engineering Review
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In cooperative multi-agent systems, group performance often depends more on the interactions between team members, rather than on the performance of any individual agent. Hence, coordination among agents is essential to optimize the group strategy. One solution which is common in the literature is to let the agents learn in a joint action space. Joint Action Learning (JAL) enables agents to explicitly take into account the actions of other agents, but has the significant drawback that the action space in which the agents must learn scales exponentially in the number of agents. Local coordination is a way for a team to coordinate while keeping communication and computational complexity low. It allows the exploitation of a specific dependency structure underlying the problem, such as tight couplings between specific agents. In this paper we investigate a novel approach to local coordination, in which agents learn this dependency structure, resulting in coordination which is beneficial to the group performance. We evaluate our approach in the context of online distributed constraint optimization problems.