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

E. Uchibe, M. Asada, K. Hosoda
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引用次数: 22

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.
基于状态向量估计的强化学习在多移动机器人环境下的合作行为获取
提出了一种基于状态向量估计的机器人行为获取方法。为了获得多机器人环境下的合作行为,每个学习机器人分别估计学习者与其他对象之间的局部预测模型。基于局部预测模型,机器人通过强化学习学习期望的行为。将所提出的方法应用于足球比赛场景中,对滚动的球和其他移动的机器人进行了很好的建模,并成功地获得了学习者的行为。给出了计算机模拟和实际实验,并进行了讨论。
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