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Cooperative behavior acquisition in multi-mobile robots environment by reinforcement learning based on state vector estimation

Eiji Uchibe, Minoru Asada, Koh Hosoda

Year
2002
Citations
24

Abstract

This paper proposes a method that acquires robots' behaviors based on the estimation of the state vectors. In order to acquire the cooperative behaviors in multi-robot environments, each learning robot estimates the local predictive model between the learner and the other objects separately. Based on the local predictive models, the robots learn the desired behaviors using reinforcement learning. The proposed method is applied to a soccer playing situation, where a rolling ball and other moving robots are well modeled and the learner's behaviors are successfully acquired by the method. Computer simulations and real experiments are shown and a discussion is given.

Keywords

Reinforcement learningRobotMobile robotComputer scienceArtificial intelligenceState (computer science)Ball (mathematics)ReinforcementRobot learningMachine learning

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