Felix Berkenkamp

Safe Model-based Reinforcement Learning with Stability Guarantees

2017 · arXiv (Cornell University)

Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a …

Verifying Controllers Against Adversarial Examples with Bayesian Optimization

2018

Recent successes in reinforcement learning have lead to the development of complex controllers for realworld robots. As these robots are deployed in safety-critical applications and interact with humans, it becomes critical to ensure safety in …