Papers
546 papers found
Learning to Correspond Dynamical Systems
Nam Hee Kim, Zhaoming Xie, Michiel Panne
Learning to Plan via Deep Optimistic Value Exploration
Tim Seyde, Wilko Schwarting, Sertac Karaman et al.
Linear Antisymmetric Recurrent Neural Networks
Signe Moe, Filippo Remonato, Esten Ingar Grøtli et al.
Localized active learning of Gaussian process state space models
Alexandre Capone, Gerrit Noske, Jonas Umlauft et al.
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
Fabio Bonassi, Enrico Terzi, Marcello Farina et al.
Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran et al.
Model-Based Reinforcement Learning with Value-Targeted Regression
Zeyu Jia, Lin Yang, Csaba Szepesvari et al.
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Homanga Bharadhwaj, Kevin Xie, Florian Shkurti
NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks
Manish Goyal, Parasara Sridhar Duggirala
NeurOpt: Neural network based optimization for building energy management and climate control
Achin Jain, Francesco Smarra, Enrico Reticcioli et al.
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan et al.
Online Data Poisoning Attacks
Xuezhou Zhang, Xiaojin Zhu, Laurent Lessard
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Lukas Hewing, Elena Arcari, Lukas P. Fröhlich et al.
On the Robustness of Data-Driven Controllers for Linear Systems
Rajasekhar Anguluri, Abed Alrahman Al Makdah, Vaibhav Katewa et al.
Optimistic robust linear quadratic dual control
Jack Umenberger, Thomas B. Schön
Parameter Optimization for Learning-based Control of Control-Affine Systems
Armin Lederer, Alexandre Capone, Sandra Hirche
Periodic Q-Learning
Donghwan Lee, Niao He
Plan2Vec: Unsupervised Representation Learning by Latent Plans
Ge Yang, Amy Zhang, Ari Morcos et al.
Planning from Images with Deep Latent Gaussian Process Dynamics
Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé et al.
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot
Napat Karnchanachari, Miguel Iglesia Valls, David Hoeller et al.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti et al.
Regret Bound for Safe Gaussian Process Bandit Optimization
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis