Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；Westlake University（西湖大学）；University of Chinese Academy of Sciences（中国科学院大学）
Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
Graduate School of Informatics, Nagoya University, Japan（名古屋大学信息学研究科）；RIKEN Center for Advanced Intelligence Project, Japan（RIKEN高级智能项目研究中心）；Graduate School of Arts and Sciences, The University of Tokyo, Japan（东京大学文学系研究科）；Project team for SIP, Japan（SIP项目团队）；Japan Agency for Marine-Earth Science and Technology, Japan（日本海洋地球科学技术机构）；Faculty of Education, Shitennoji University, Japan（世田谷大学教育学部）；Graduate School of Engineering, The University of Tokyo, Japan（东京大学工学研究科）；Graduate School of Science and Technology, Niigata University, Japan（新潟大学科学技术研究科）；Graduate School of Science, Nagoya University, Japan（名古屋大学理学研究科）；Principles of Informatics Research Division, National Institute of Informatics, Japan（信息学原理研究部门，日本信息处理技术研究所）；Graduate School of Information Science, The University of Osaka, Japan（大阪大学信息科学研究科）
Department of Electrical and Computer Engineering, University of Minnesota（明尼苏达大学电气与计算机工程系）；Laboratory for Computational Sensing and Robotics, Johns Hopkins University（约翰霍普金斯大学计算感知与机器人实验室）；Federated Department of Biological Sciences, New Jersey Institute of Technology（新泽西理工学院联合生物科学系）；Department of Mathematics, Cornell University（康奈尔大学数学系）；Department of Mathematics and Statistics, University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校数学与统计学系）；Department of Mechanical Engineering, Johns Hopkins University（约翰霍普金斯大学机械工程系）
