research
∙
05/02/2023
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not ...
share
research
∙
07/04/2022
Safe Reinforcement Learning via Confidence-Based Filters
Ensuring safety is a crucial challenge when deploying reinforcement lear...
research
∙
01/24/2022
Constrained Policy Optimization via Bayesian World Models
Improving sample-efficiency and safety are crucial challenges when deplo...
research
∙
03/18/2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
In real-world tasks, reinforcement learning (RL) agents frequently encou...
research
∙
02/10/2021
Risk-Averse Offline Reinforcement Learning
Training Reinforcement Learning (RL) agents in high-stakes applications ...
research
∙
10/21/2020
Logistic Q-Learning
We propose a new reinforcement learning algorithm derived from a regular...
research
∙
06/19/2020
Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
We present the first approach for learning – from a single trajectory – ...
research
∙
06/15/2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Model-based reinforcement learning algorithms with probabilistic dynamic...
research
∙
10/28/2019
Adaptive Sampling for Stochastic Risk-Averse Learning
We consider the problem of training machine learning models in a risk-av...
research
∙
07/16/2019
Structured Variational Inference in Unstable Gaussian Process State Space Models
Gaussian processes are expressive, non-parametric statistical models tha...
research
∙
06/28/2019
Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning
We tune one of the most common heating, ventilation, and air conditionin...
research
∙
03/29/2019
Online Variance Reduction with Mixtures
Adaptive importance sampling for stochastic optimization is a promising ...
research
∙
06/19/2018
