Scenario Generalization of Data-driven Imitation Models in Crowd Simulation
暂无分享,去创建一个
[1] Dinesh Manocha,et al. Velocity-based modeling of physical interactions in multi-agent simulations , 2013, SCA '13.
[2] Jungdam Won,et al. Crowd simulation by deep reinforcement learning , 2018, MIG.
[3] Dinesh Manocha,et al. Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective , 2013, Modeling, Simulation and Visual Analysis of Crowds.
[4] Mykel J. Kochenderfer,et al. Imitating driver behavior with generative adversarial networks , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).
[5] Helbing,et al. Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[6] Glenn Reinman,et al. SteerBench: a benchmark suite for evaluating steering behaviors , 2009, Comput. Animat. Virtual Worlds.
[7] Soraia Raupp Musse,et al. Optimal Group Distribution based on Thermal and Psycho-Social Aspects , 2019, CASA.
[8] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[9] Lisa Torrey,et al. Crowd Simulation Via Multi-Agent Reinforcement Learning , 2010, AIIDE.
[10] Dinh Q. Phung,et al. On Deep Domain Adaptation: Some Theoretical Understandings , 2018 .
[11] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[12] Qunsheng Peng,et al. Group Modeling: A Unified Velocity‐Based Approach , 2017, Comput. Graph. Forum.
[13] Paolo Fiorini,et al. Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..
[14] Prashant Doshi,et al. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress , 2018, Artif. Intell..
[15] Dinesh Manocha,et al. Modeling, Simulation and Visual Analysis of Crowds , 2013, The International Series in Video Computing.
[16] Luiselena Casadiego Bastidas. Social crowd controllers using reinforcement learning methods , 2014 .
[17] Gang Qiao,et al. The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation , 2017, AAAI.
[18] Dinesh Manocha,et al. Reciprocal n-Body Collision Avoidance , 2011, ISRR.
[19] Xiaodong Gu,et al. Data-Driven and Collision-Free Hybrid Crowd Simulation Model for Real Scenario , 2018, ICONIP.
[20] Philip Chan,et al. Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..
[22] José M. F. Moura,et al. Adversarial Multiple Source Domain Adaptation , 2018, NeurIPS.
[23] Vladimir Pavlovic,et al. Characterizing the relationship between environment layout and crowd movement using machine learning , 2017, MIG.
[24] Michael H. Bowling,et al. Apprenticeship learning using linear programming , 2008, ICML '08.
[25] Tsuyoshi Murata,et al. {m , 1934, ACML.
[26] Glenn Reinman,et al. SteerBench: a benchmark suite for evaluating steering behaviors , 2009 .
[27] Dinesh Manocha,et al. Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory , 2012, I3D '12.
[28] Vladimir Pavlovic,et al. Filling in the blanks: reconstructing microscopic crowd motion from multiple disparate noisy sensors , 2016, 2016 IEEE Winter Applications of Computer Vision Workshops (WACVW).
[29] John R. Hershey,et al. Single-Channel Multitalker Speech Recognition , 2010, IEEE Signal Processing Magazine.
[30] J. Andrew Bagnell,et al. Efficient Reductions for Imitation Learning , 2010, AISTATS.
[31] Glenn Reinman,et al. Scenario space: characterizing coverage, quality, and failure of steering algorithms , 2011, SCA '11.
[32] Jean-Baptiste Mouret,et al. Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[33] Stefano Ermon,et al. Generative Adversarial Imitation Learning , 2016, NIPS.
[34] Trung Le,et al. Theoretical Perspective of Deep Domain Adaptation , 2018, ArXiv.
[35] Peter Stone,et al. Behavioral Cloning from Observation , 2018, IJCAI.
[36] Sergey Levine,et al. A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.
[37] Anind K. Dey,et al. Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.
[38] Ioannis Karamouzas,et al. Universal power law governing pedestrian interactions. , 2014, Physical review letters.
[39] Silvio Savarese,et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Hao Zhang,et al. Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[41] Norman I. Badler,et al. Virtual Crowds: Steps Toward Behavioral Realism , 2015, Virtual Crowds: Steps Toward Behavioral Realism.
[42] Jia Pan,et al. Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation , 2016, IEEE Robotics and Automation Letters.
[43] Soraia Raupp Musse,et al. Crowd Analysis Using Computer Vision Techniques , 2010, IEEE Signal Processing Magazine.
[44] Vicsek,et al. Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.