Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Information Sciences: an International Journal
Editorial: Intelligent robotics and neuroscience
Robotics and Autonomous Systems
Linked multi-component mobile robots: Modeling, simulation and control
Robotics and Autonomous Systems
Linked multicomponent robotic systems: basic assessment of linking element dynamical effect
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Concurrent modular Q-learning with local rewards on linked multi-component robotic systems
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
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When conventional Q-Learning is applied to Multi-Component Robotic Systems (MCRS), increasing the number of components produces an exponential growth of state storage requirements. Modular approaches make the state size growth polynomial on the number of components, making more manageable its representation and manipulation. In this article, we give the first steps towards a modular Q-learning approach to learn the distributed control of a Linked MCRS, which is a specific type of MCRSs in which the individual robots are linked by a passive element. We have chosen a paradigmatic application of this kind of systems: a set of robots carrying the tip of a hose from some initial position to a desired goal. The hose dynamics is simplified to be a distance constraint on the robots positions.