Antonio Filieri

Safe Reinforcement Learning under Regularized Probabilistic Counterexample Guidance

A counterexample-guided reinforcement learning method that navigates safe exploration in autonomous systems without prior knowledge, even when safety and optimality conflict, and a novel belief-based regularization method to address the distributional shift between online and offline learning and to balance optimization and safety.