Search Results for author:
Found 15 papers, 6 papers with code
Get Back Here: Robust Imitation by Return-to-Distribution Planning
We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version.
Safe Reinforcement Learning via Confidence-Based Filters
Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems.
Gradient-Based Trajectory Optimization With Learned Dynamics
In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car, and gives good performance in combination with trajectory optimization methods.
Constrained Policy Optimization via Bayesian World Models
Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications.
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time.
Deep Reinforcement Learning
Model-based Reinforcement Learning
+2
Risk-Averse Offline Reinforcement Learning
We demonstrate empirically that in the presence of natural distribution-shifts, O-RAAC learns policies with good average performance.
Logistic Q-Learning
We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs.
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
The principal task to control dynamical systems is to ensure their stability.
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Based on this theoretical foundation, we show how optimistic exploration can be easily combined with state-of-the-art reinforcement learning algorithms and different probabilistic models.
Model-based Reinforcement Learning
reinforcement-learning
+2
Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models
Finally, we show experimentally that our learning algorithm performs well in stable and unstable real systems with hidden states.
Adaptive Sampling for Stochastic Risk-Averse Learning
In high-stakes machine learning applications, it is crucial to not only perform well on average, but also when restricted to difficult examples.
Structured Variational Inference in Unstable Gaussian Process State Space Models
Finally, we show experimentally that our learning algorithm performs well in stable and unstable real systems with hidden states.
Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning
Furthermore, this successful attempt paves the way for further use at different levels of HVAC systems, with promising energy, operational, and commissioning costs savings, and it is a practical demonstration of the positive effects that Artificial Intelligence can have on environmental sustainability.
Online Variance Reduction with Mixtures
Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction.
Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations
To this end, we introduce a novel estimation algorithm that explicitly trades off bias and variance to optimally reduce the overall estimation error.
