Multiagent reinforcement learning using Non-Parametric Approximation
Aprendizaje por reforzamiento para sistemas multiagentes utilizando Aproximación No Paramétrica
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This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.
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