title: Robotic action acquisition with cognitive biases in coarse-grained state space.
abstract: Some of the authors have previously proposed a cognitively inspired reinforcement learning architecture (LS-Q) that mimics cognitive biases in humans. LS-Q adaptively learns under uniform, coarse-grained state division and performs well without parameter tuning in a giant-swing robot task. However, these results were shown only in simulations. In this study, we test the validity of the LS-Q implemented in a robot in a real environment. In addition, we analyze the learning process to elucidate the mechanism by which the LS-Q adaptively learns under the partially observable environment. We argue that the LS-Q may be a versatile reinforcement learning architecture, which is, despite its simplicity, easily applicable and does not require well-prepared settings.
authors.affiliation: College of Industrial Technology, Nihon University, 1-2-1, Izumi, Narashino, Chiba, 275-8575, Japan. Electronic address: dduragami@gmail.com.
authors.forename: Daisuke
authors.initials: D
authors.lastname: Uragami
authors.affiliation: Graduate School of Advanced Science and Technology, Tokyo Denki University, Hatoyama, Hiki, Saitama, 350-0394, Japan. Electronic address: yu.kohno02@gmail.com.
authors.forename: Yu
authors.initials: Y
authors.lastname: Kohno
authors.affiliation: School of Science and Technology, Tokyo Denki University, Hatoyama, Hiki, Saitama, 350-0394, Japan. Electronic address: tatsuji.takahashi@gmail.com.
authors.forename: Tatsuji
authors.initials: T
authors.lastname: Takahashi
mesh_terms: D001185:Artificial Intelligence; D003071:Cognition; D012371:Robotics / Q000295:instrumentation* / Q000379:methods
keywords: Acrobot; Biologically inspired cognitive architecture; Giant-swing robot; Loosely symmetric model; Partially observable markov decision process; Q-learning