Student-t policy in reinforcement learning to acquire global optimum of robot control
暂无分享,去创建一个
[1] Surya P. N. Singh,et al. V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[2] Toshio Fukuda,et al. Selection Algorithm for Locomotion Based on the Evaluation of Falling Risk , 2015, IEEE Transactions on Robotics.
[3] Andrew Gordon Wilson,et al. Student-t Processes as Alternatives to Gaussian Processes , 2014, AISTATS.
[4] Luisa Canal,et al. A normal approximation for the chi-square distribution , 2005, Comput. Stat. Data Anal..
[5] Pieter Abbeel,et al. Constrained Policy Optimization , 2017, ICML.
[6] Joonho Lee,et al. Learning agile and dynamic motor skills for legged robots , 2019, Science Robotics.
[7] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[8] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[9] Sebastian Scherer,et al. Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution , 2017, ICML.
[10] Sergey Levine,et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , 2018, ICML.
[11] Sergey Levine,et al. High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.
[12] Avinash C. Kak,et al. A Novel Parameter Estimation Algorithm for the Multivariate t-Distribution and Its Application to Computer Vision , 2010, ECCV.
[13] Jeremy MG Taylor,et al. Robust Statistical Modeling Using the t Distribution , 1989 .
[14] Jan Peters,et al. Hierarchical Relative Entropy Policy Search , 2014, AISTATS.
[15] Richard S. Sutton,et al. Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[16] Takamitsu Matsubara,et al. Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states , 2017, Neural Networks.
[17] Sham M. Kakade,et al. A Natural Policy Gradient , 2001, NIPS.
[18] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[19] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[20] Nuttapong Chentanez,et al. Intrinsically Motivated Reinforcement Learning , 2004, NIPS.
[21] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[22] Takamitsu Matsubara,et al. Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation , 2019, Robotics Auton. Syst..
[23] Weikuan Jia,et al. Applications of asynchronous deep reinforcement learning based on dynamic updating weights , 2018, Applied Intelligence.
[24] Patrick M. Pilarski,et al. True Online Temporal-Difference Learning , 2015, J. Mach. Learn. Res..
[25] Yuval Tassa,et al. Emergence of Locomotion Behaviours in Rich Environments , 2017, ArXiv.
[26] Reinaldo B. Arellano-Valle. On the information matrix of the multivariate skew-t model , 2010 .
[27] Philip Thomas,et al. Bias in Natural Actor-Critic Algorithms , 2014, ICML.
[28] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[29] Filip De Turck,et al. VIME: Variational Information Maximizing Exploration , 2016, NIPS.
[30] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[31] Christopher M. Bishop,et al. Robust Bayesian Mixture Modelling , 2005, ESANN.
[32] Guy Lever,et al. Deterministic Policy Gradient Algorithms , 2014, ICML.
[33] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[34] Frederic Bartumeus,et al. ANIMAL SEARCH STRATEGIES: A QUANTITATIVE RANDOM‐WALK ANALYSIS , 2005 .
[35] Andrew Y. Ng,et al. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.
[36] Richard E. Turner,et al. Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning , 2017, NIPS.
[37] Javier E. Contreras-Reyes. Asymptotic form of the Kullback–Leibler divergence for multivariate asymmetric heavy-tailed distributions , 2014 .
[38] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[39] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[40] T. Takenaka,et al. The development of Honda humanoid robot , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).
[41] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.