Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning of coordination: exploiting sparse interactions in multiagent systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning multi-agent state space representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Recent research has demonstrated that considering local interactions among agents in specific parts of the state space, is a successful way of simplifying the multi-agent learning process. By taking into account other agents only when a conflict is possible, an agent can significantly reduce the state-action space in which it learns. Current approaches, however, consider only the immediate rewards for detecting conflicts. This restriction is not suitable for realistic systems, where rewards can be delayed and often conflicts between agents become apparent only several time-steps after an action has been taken. In this paper, we contribute a reinforcement learning algorithm that learns where a strategic interaction among agents is needed, several time-steps before the conflict is reflected by the (immediate) reward signal.