{"list":"other","total":56,"rows":[{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1606.03476","title":"Generative Adversarial Imitation Learning","created":"2016-06-10","cited_by_count":1597},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"1603.04467","title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","created":"2016-03-14","cited_by_count":664},{"id":"1805.01954","title":"Behavioral Cloning from Observation","created":"2018-05-04","cited_by_count":492},{"id":"1806.06877","title":"A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress","created":"2018-06-18","cited_by_count":298},{"id":"1506.07365","title":"Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images","created":"2015-06-24","cited_by_count":274},{"id":"1605.07157","title":"Unsupervised Learning for Physical Interaction through Video Prediction","created":"2016-05-23","cited_by_count":260},{"id":"1904.06387","title":"Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations","created":"2019-04-12","cited_by_count":260},{"id":"1812.03596","title":"Task-Free Continual Learning","created":"2018-12-10","cited_by_count":221},{"id":"1907.04490","title":"Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning","created":"2019-07-10","cited_by_count":212},{"id":"1903.12394","title":"Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems","created":"2019-03-29","cited_by_count":205},{"id":"1810.13400","title":"Differentiable MPC for End-to-end Planning and Control","created":"2018-10-31","cited_by_count":189},{"id":"1909.12077","title":"Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control","created":"2019-09-26","cited_by_count":184},{"id":"1510.09142","title":"Learning Continuous Control Policies by Stochastic Value Gradients","created":"2015-10-30","cited_by_count":162},{"id":"1603.00748","title":"Continuous Deep Q-Learning with Model-based Acceleration","created":"2016-03-02","cited_by_count":157},{"id":"1806.05635","title":"Self-Imitation Learning","created":"2018-06-14","cited_by_count":138},{"id":"1602.04450","title":"Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics","created":"2016-02-14","cited_by_count":134},{"id":"2107.10043","title":"KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics","created":"2021-07-21","cited_by_count":132},{"id":"1409.6358","title":"Dynamic mode decomposition with control","created":"2014-09-22","cited_by_count":129},{"id":"1901.09387","title":"Imitation Learning from Imperfect Demonstration","created":"2019-01-27","cited_by_count":114},{"id":"2305.18803","title":"Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors","created":"2023-05-30","cited_by_count":113},{"id":"1902.02186","title":"Distilling Policy Distillation","created":"2019-02-06","cited_by_count":84},{"id":"1710.09513","title":"Maximum Principle Based Algorithms for Deep Learning","created":"2017-10-26","cited_by_count":78},{"id":"1807.01083","title":"A Mean-Field Optimal Control Formulation of Deep Learning","created":"2018-07-03","cited_by_count":67},{"id":"1605.09128","title":"Control of Memory, Active Perception, and Action in Minecraft","created":"2016-05-30","cited_by_count":61},{"id":"2102.06483","title":"Scalable Bayesian Inverse Reinforcement Learning","created":"2021-02-12","cited_by_count":56},{"id":"1611.07422","title":"Deep Learning Approximation for Stochastic Control Problems","created":"2016-11-02","cited_by_count":55},{"id":"1602.07647","title":"Generalizing Koopman Theory to allow for inputs and control","created":"2016-02-24","cited_by_count":53},{"id":"1912.12970","title":"Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework","created":"2019-12-30","cited_by_count":52},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37},{"id":"1910.09349","title":"Variational Integrator Networks for Physically Structured Embeddings","created":"2019-10-21","cited_by_count":36},{"id":"1708.07827","title":"Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study","created":"2017-08-25","cited_by_count":35},{"id":"1904.05657","title":"Deep learning as optimal control problems: models and numerical methods","created":"2019-04-11","cited_by_count":34},{"id":"1906.05194","title":"Active Learning of Dynamics for Data-Driven Control Using Koopman Operators","created":"2019-06-12","cited_by_count":32},{"id":"1804.00645","title":"Universal Planning Networks","created":"2018-04-02","cited_by_count":30},{"id":"2103.06525","title":"Implicit energy regularization of neural ordinary-differential-equation control","created":"2021-03-11","cited_by_count":29},{"id":"1801.07780","title":"Online Optimization with Predictions and Switching Costs: Fast Algorithms and the Fundamental Limit","created":"2018-01-23","cited_by_count":27},{"id":"1803.01299","title":"An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks","created":"2018-03-04","cited_by_count":26},{"id":"2105.14937","title":"Safe Pontryagin Differentiable Programming","created":"2021-05-31","cited_by_count":26},{"id":"1803.00512","title":"Composable Planning with Attributes","created":"2018-03-01","cited_by_count":21},{"id":"1706.09597","title":"Path Integral Networks: End-to-End Differentiable Optimal Control","created":"2017-06-29","cited_by_count":16},{"id":"2011.11841","title":"A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization","created":"2020-11-24","cited_by_count":16},{"id":"1802.05803","title":"MPC-Inspired Neural Network Policies for Sequential Decision Making","created":"2018-02-15","cited_by_count":13},{"id":"2210.14816","title":"Deep Subspace Encoders for Nonlinear System Identification","created":"2022-10-26","cited_by_count":13},{"id":"2008.02159","title":"Learning from Sparse Demonstrations","created":"2020-08-05","cited_by_count":9},{"id":"2007.12536","title":"Performance-Driven Cascade Controller Tuning with Bayesian Optimization","created":"2020-07-24","cited_by_count":8},{"id":"1905.09076","title":"Selection dynamics for deep neural networks","created":"2019-05-22","cited_by_count":6},{"id":"2011.15014","title":"Learning from Human Directional Corrections","created":"2020-11-30","cited_by_count":6},{"id":"1803.07696","title":"Inverse Optimal Control from Incomplete Trajectory Observations","created":"2018-03-21","cited_by_count":4},{"id":"2304.00100","title":"A Data-Driven Approach for Inverse Optimal Control","created":"2023-03-31","cited_by_count":3},{"id":"2309.15498","title":"A Control Theoretical Approach to Online Constrained Optimization","created":"2023-09-27","cited_by_count":2},{"id":"1709.04889","title":"Control-Oriented Learning on the Fly","created":"2017-09-14","cited_by_count":1},{"id":"2205.13932","title":"Internal Model-Based Online Optimization","created":"2022-05-27","cited_by_count":1},{"id":"2305.15188","title":"Optimal Control of Nonlinear Systems with Unknown Dynamics","created":"2023-05-24","cited_by_count":1}],"next_cursor":null}