On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
暂无分享,去创建一个
[1] C. D. Vecchio,et al. Inverse Data-Driven Optimal Control for Nonlinear Stochastic Non-stationary Systems , 2023, ArXiv.
[2] L. Rodrigues. Inverse Optimal Control with Discount Factor for Continuous and Discrete-Time Control-Affine Systems and Reinforcement Learning , 2022, 2022 IEEE 61st Conference on Decision and Control (CDC).
[3] F. Lewis,et al. Inverse reinforcement learning for multi-player noncooperative apprentice games , 2022, Autom..
[4] Siddharth H. Nair,et al. Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using Model Predictive Control , 2022, 2022 IEEE 61st Conference on Decision and Control (CDC).
[5] R. Kamalapurkar,et al. Model-based inverse reinforcement learning for deterministic systems , 2022, Autom..
[6] Émiland Garrabé,et al. Probabilistic design of optimal sequential decision-making algorithms in learning and control , 2022, Annu. Rev. Control..
[7] Mac Schwager,et al. Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions , 2021, IEEE Transactions on Robotics.
[8] Anders Rantzer,et al. On Cost Design in Applications of Optimal Control , 2021, IEEE Control Systems Letters.
[9] Dimitri Bertsekas,et al. Multiagent Reinforcement Learning: Rollout and Policy Iteration , 2021, IEEE/CAA Journal of Automatica Sinica.
[10] Roy S. Smith,et al. Maximum Likelihood Estimation in Data-Driven Modeling and Control , 2020, IEEE Transactions on Automatic Control.
[11] Mohsen Davoudi,et al. From inverse optimal control to inverse reinforcement learning: A historical review , 2020, Annu. Rev. Control..
[12] Yumiharu Nakano. Inverse stochastic optimal controls , 2020, Autom..
[13] Giovanni Russo,et al. On a probabilistic approach to synthesize control policies from example datasets , 2020, Autom..
[14] Marcelo Menezes Morato,et al. An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil , 2020, Annual Reviews in Control.
[15] Siddharth Mayya,et al. The Robotarium: Globally Impactful Opportunities, Challenges, and Lessons Learned in Remote-Access, Distributed Control of Multirobot Systems , 2020, IEEE Control Systems.
[16] Sean P. Meyn,et al. Kullback-Leibler-Quadratic Optimal Control of Flexible Power Demand , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).
[17] Khac Duc Do,et al. Inverse optimal control of stochastic systems driven by Lévy processes , 2019, Autom..
[18] Lillian J. Ratliff,et al. Inverse Risk-Sensitive Reinforcement Learning , 2017, IEEE Transactions on Automatic Control.
[19] Karl Henrik Johansson,et al. Mapless indoor localization by trajectory learning from a crowd , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).
[20] Stephen P. Boyd,et al. CVXPY: A Python-Embedded Modeling Language for Convex Optimization , 2016, J. Mach. Learn. Res..
[21] Sergey Levine,et al. Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization , 2016, ICML.
[22] Rebecca Willett,et al. Online Markov Decision Processes With Kullback–Leibler Control Cost , 2014, IEEE Transactions on Automatic Control.
[23] Stefan Schaal,et al. Learning objective functions for manipulation , 2013, 2013 IEEE International Conference on Robotics and Automation.
[24] Sergey Levine,et al. Continuous Inverse Optimal Control with Locally Optimal Examples , 2012, ICML.
[25] Sergey Levine,et al. Nonlinear Inverse Reinforcement Learning with Gaussian Processes , 2011, NIPS.
[26] Emanuel Todorov,et al. Inverse Optimal Control with Linearly-Solvable MDPs , 2010, ICML.
[27] Emanuel Todorov,et al. Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.
[28] Stefan Schaal,et al. Path integral-based stochastic optimal control for rigid body dynamics , 2009, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning.
[29] Vicenç Gómez,et al. Optimal control as a graphical model inference problem , 2009, Machine Learning.
[30] Anind K. Dey,et al. Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.
[31] Emanuel Todorov,et al. Linearly-solvable Markov decision problems , 2006, NIPS.
[32] Tatiana V. Guy,et al. Fully probabilistic control design , 2006, Syst. Control. Lett..
[33] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[34] M. Krstić,et al. Stochastic nonlinear stabilization—II: inverse optimality , 1997 .
[35] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[36] Miroslav Kárný,et al. Towards fully probabilistic control design , 1996, Autom..
[37] J. A. Bryson. Optimal control-1950 to 1985 , 1996 .
[38] A. Charnes,et al. The role of duality in optimization problems involving entropy functionals with applications to information theory , 1988 .
[39] Mao Shan,et al. Kalman filtering under unknown inputs and norm constraints , 2021, Autom..
[40] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[41] Tanja Sušec,et al. Historical Review , 1917, Acta Cytologica.