15 citations · 35 across the 5 of their papers we have counts for
10 papers
Constrained Policy Optimization via Bayesian World Models
Yarden As, Ilnura Usmanova, Sebastian Curi +1
Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based a…
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
Sebastian Curi, Ilija Bogunovic, Andreas Krause
In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time. To ensure reliable performance, the RL agents nee…
Risk-Averse Offline Reinforcement Learning
Núria Armengol Urpí, Sebastian Curi, Andreas Krause
Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration. Thus, the agent can only use data previo…
Logistic Q-Learning
Joan Bas-Serrano, Sebastian Curi, Andreas Krause +1
We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The method is closely related to the classic…
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
Lenart Treven, Sebastian Curi, Mojmir Mutny +1
The principal task to control dynamical systems is to ensure their stability. When the system is unknown, robust approaches are promising since they aim to stabilize a large set of…
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian Curi, Felix Berkenkamp, Andreas Krause
Model-based reinforcement learning algorithms with probabilistic dynamical models are amongst the most data-efficient learning methods. This is often attributed to their ability to…