- Safe Exploration in Continuous Action Spaces
- Safe Exploration for Interactive Machine Learning
- Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments
- A Lyapunov-based Approach to Safe Reinforcement Learning
- Constrained Policy Optimization
- End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
- Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
- Convergent Policy Optimization for Safe Reinforcement Learning
- Lyapunov-based Safe Policy Optimization for Continuous Control
- Neural Lyapunov Control
- CAQL: CONTINUOUS ACTION Q-LEARNING
- Safe Model-based Reinforcement Learning with Stability Guarantees
- The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
- Safe Learning of Regions of Attraction for Uncertain, Nonlinear Systems with Gaussian Processes
- Code