Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Robotic Systems
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A study of mechanisms for improving robotic group performance
Artificial Intelligence
Multiagent reinforcement learning for a planetary exploration multirobot system
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Multirobot systems: a classification focused on coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multi-robot systems are one of the most challenging problems in autonomous robots. Teams of homogeneous or heterogeneous robots must be able to solve complex tasks. Sometimes the tasks have a cooperative basis in which the global objective is shared by all the robots. In other situations, the robots can be different and even contradictory goals, defining a kind of competitive problems. The multi-robot systems domain is a perfect example in which the uncertainty and vagueness in sensor readings and robot odometry must be handled by using techniques which can deal with this kind of imprecise data. In this paper we introduce the use of Reinforcement Learning techniques for solving cooperative problems in teams of homogeneous robots. As an example, the problem of maintaining a mobile robots formation is studied.