Safe Exploration

Safe exploration in reinforcement learning focuses on enabling agents to learn optimal policies while rigorously avoiding unsafe actions or states during the learning process. Current research emphasizes developing algorithms and model architectures (e.g., model-predictive control, Bayesian methods, and Lagrangian approaches) that incorporate safety constraints, often using techniques like control barrier functions or risk-aware exploration strategies. This field is crucial for deploying reinforcement learning in real-world applications, particularly in robotics and autonomous systems, where safety is paramount and trial-and-error learning could have severe consequences.