On a probabilistic approach to synthesize control policies from example datasets

[1]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[2]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[3]  A. Tucker,et al.  Linear Inequalities And Related Systems , 1956 .

[4]  Chun. Loo,et al.  BAYESIAN APPROACH TO SYSTEM IDENTIFICATION , 1981 .

[5]  W. J. Studden,et al.  Optimal Experimental Designs , 1966 .

[6]  R. Rockafellar,et al.  Duality and stability in extremum problems involving convex functions. , 1967 .

[7]  Ky Fan,et al.  On infinite systems of linear inequalities , 1968 .

[8]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[9]  Ja. Z. Cypkin.  Learning models , 1971, Kybernetika.

[10]  K. Fan.  Two applications of a consistency theorem for systems of linear inequalities , 1975 .

[11]  K. L. Hiebert,et al.  Solving Systems of Linear Equations and Inequalities , 1980 .

[12]  Y. Censor,et al.  New methods for linear inequalities , 1982 .

[13]  A. Charnes,et al.  The role of duality in optimization problems involving entropy functionals with applications to information theory , 1988 .

[14]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[15]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[16]  Miroslav Kárný,et al.  Towards fully probabilistic control design , 1996, Autom..

[17]  G. Tallini,et al.  ON THE EXISTENCE OF , 1996 .

[18]  J. A. Bryson.  Optimal control-1950 to 1985 , 1996 .

[19]  The Synthesis , 1996, Ideas in God According to Saint Thomas Aquinas.

[20]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[21]  Analog Vlsi,et al.  On the Design of , 2000 .

[22]  Tryphon T. Georgiou,et al.  Kullback-Leibler approximation of spectral density functions , 2003, IEEE Trans. Inf. Theory.

[23]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[24]  R. Boţ,et al.  Duality for optimization problems with entropy-like objective functions , 2005 .

[25]  J. Andrew Bagnell,et al.  Maximum margin planning , 2006, ICML.

[26]  Tatiana V. Guy,et al.  Fully probabilistic control design , 2006, Syst. Control. Lett..

[27]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[28]  Michele Pavon,et al.  On the Georgiou-Lindquist approach to constrained Kullback-Leibler approximation of spectral densities , 2006, IEEE Transactions on Automatic Control.

[29]  Emanuel Todorov,et al.  Linearly-solvable Markov decision problems , 2006, NIPS.

[30]  Stefan Hildebrandt,et al.  Calculus of Variations II , 2006 .

[31]  Paolo Rapisarda,et al.  On the linear quadratic data-driven control , 2007, 2007 European Control Conference (ECC).

[32]  Eyal Amir,et al.  Bayesian Inverse Reinforcement Learning , 2007, IJCAI.

[33]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[34]  Miroslav Kárný,et al.  Stochastic control optimal in the Kullback sense , 2008, Kybernetika.

[35]  Shankar P. Bhattacharyya,et al.  Controller Synthesis Free of Analytical Models: Three Term Controllers , 2008, IEEE Transactions on Automatic Control.

[36]  Emanuel Todorov,et al.  Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.

[37]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[38]  David Silver,et al.  Learning to search: Functional gradient techniques for imitation learning , 2009, Auton. Robots.

[39]  Z. Hou,et al.  On Data-driven Control Theory: the State of the Art and Perspective: On Data-driven Control Theory: the State of the Art and Perspective , 2009 .

[40]  Hou Zhong,et al.  On Data-driven Control Theory:the State of the Art and Perspective , 2009 .

[41]  Eduardo F. Morales,et al.  An Introduction to Reinforcement Learning , 2011 .

[42]  Rebecca Willett,et al.  Online Markov Decision Processes With Kullback–Leibler Control Cost , 2014, IEEE Transactions on Automatic Control.

[43]  Miroslav Kárný,et al.  Axiomatisation of fully probabilistic design , 2012, Inf. Sci..

[44]  Vicenç Gómez,et al.  Optimal control as a graphical model inference problem , 2009, Machine Learning.

[45]  Michèle Basseville,et al.  Divergence measures for statistical data processing - An annotated bibliography , 2013, Signal Process..

[46]  Shreyas Sundaram,et al.  Resilient Asymptotic Consensus in Robust Networks , 2013, IEEE Journal on Selected Areas in Communications.

[47]  Claire J. Tomlin,et al.  A probabilistic approach to planning and control in autonomous urban driving , 2013, 52nd IEEE Conference on Decision and Control.

[48]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[49]  E. Walter.  Solving Systems of Linear Equations , 2014 .

[50]  Mohit Singh,et al.  Entropy, optimization and counting , 2013, STOC.

[51]  Randa Herzallah,et al.  Fully probabilistic control for stochastic nonlinear control systems with input dependent noise , 2015, Neural Networks.

[52]  Harald Waschl,et al.  Short Term Prediction of a Vehicle's Velocity Trajectory Using ITS , 2015 .

[53]  María M. Seron,et al.  From vehicular platoons to general networked systems: String stability and related concepts , 2017, Annu. Rev. Control..

[54]  Peter Englert,et al.  Inverse KKT - Learning Cost Functions of Manipulation Tasks from Demonstrations , 2017, ISRR.

[55]  Siavash Fakhimi Derakhshan,et al.  Lazy Fully Probabilistic Design: Application Potential , 2017, EUMAS/AT.

[56]  Luigi del Re,et al.  Autonomous overtaking using stochastic model predictive control , 2017, 2017 11th Asian Control Conference (ASCC).

[57]  Lorenzo Fagiano,et al.  Data-driven control of nonlinear systems: An on-line direct approach , 2017, Autom..

[58]  Luigi del Re,et al.  Risk functions oriented autonomous overtaking , 2017, 2017 11th Asian Control Conference (ASCC).

[59]  Richard M. Murray,et al.  Future systems and control research in synthetic biology , 2018, Annu. Rev. Control..

[60]  Alberto Bemporad,et al.  Data-based predictive control via direct weight optimization , 2018 .

[61]  Francesco Borrelli,et al.  Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework , 2016, IEEE Transactions on Automatic Control.

[62]  Kim Peter Wabersich,et al.  Scalable synthesis of safety certificates from data with application to learning-based control , 2018, 2018 European Control Conference (ECC).

[63]  Robert Shorten,et al.  A Vehicle-in-the-Loop Emulation Platform for Demonstrating Intelligent Transportation Systems , 2019 .

[64]  Hao Liu,et al.  Learning Policies for Markov Decision Processes From Data , 2017, IEEE Transactions on Automatic Control.

[65]  Luigi del Re,et al.  Microscopic driving behavior modelling at highway entrances using Bayesian network , 2019, 2019 American Control Conference (ACC).

[66]  Giovanni Russo,et al.  On robust stability of fully probabilistic control with respect to data-driven model uncertainties , 2019, 2019 18th European Control Conference (ECC).

[67]  John Lygeros,et al.  Regularized and Distributionally Robust Data-Enabled Predictive Control , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[68]  Yannick Schroecker,et al.  Imitating Latent Policies from Observation , 2018, ICML.

[69]  Giacomo Baggio,et al.  On the Existence of a Solution to a Spectral Estimation Problem à la Byrnes–Georgiou–Lindquist , 2017, IEEE Transactions on Automatic Control.

[70]  Jeongseok Seo,et al.  Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus , 2019, Electronics.

[71]  Tingting Xu,et al.  Learning models for writing better doctor prescriptions , 2019, 2019 18th European Control Conference (ECC).

[72]  John Lygeros,et al.  Data-Enabled Predictive Control: In the Shallows of the DeePC , 2018, 2019 18th European Control Conference (ECC).

[73]  Mayank Bansal,et al.  ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst , 2018, Robotics: Science and Systems.

[74]  Alexandre S. Bazanella,et al.  Data-Driven LQR Control Design , 2018, IEEE Control Systems Letters.

[75]  Christopher D. McKinnon,et al.  Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems With Unknown and Changing Dynamics Performing Repetitive Tasks , 2018, IEEE Robotics and Automation Letters.

[76]  Giacomo Baggio,et al.  Data-Driven Minimum-Energy Controls for Linear Systems , 2019, IEEE Control Systems Letters.

[77]  On the synthesis of control policies from example datasets , 2020 .

[78]  C. D. Persis,et al.  Willems’ Fundamental Lemma for State-Space Systems and Its Extension to Multiple Datasets , 2020, IEEE Control Systems Letters.

[79]  Xavier Bombois,et al.  Data informativity for the open-loop identification of MIMO systems in the prediction error framework , 2020, Autom..

[80]  M. Kanat Camlibel,et al.  Data Informativity: A New Perspective on Data-Driven Analysis and Control , 2019, IEEE Transactions on Automatic Control.

[81]  Pietro Tesi,et al.  Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness , 2019, IEEE Transactions on Automatic Control.

[82]  Convex Optimization on Functionals of Probability Densities , 2020, ArXiv.

[83]  Hao Liu,et al.  Learning from animals: How to Navigate Complex Terrains , 2020, PLoS Comput. Biol..

[84]  Dimitri Bertsekas,et al.  Multiagent Reinforcement Learning: Rollout and Policy Iteration , 2021, IEEE/CAA Journal of Automatica Sinica.

[85]  Henk J. van Waarde,et al.  Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control , 2022, IEEE Control Systems Letters.

[86]  Giovanni Russo,et al.  On the Crowdsourcing of Behaviors for Autonomous Agents , 2020, IEEE Control Systems Letters.

[87]  Giovanni Russo,et al.  On the Design of Autonomous Agents From Multiple Data Sources , 2021, IEEE Control. Syst. Lett..

[88]  Soon-Jo Chung,et al.  Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems , 2020, IEEE Robotics Autom. Lett..