Safe Reinforcement Learning Algorithm

Safe reinforcement learning (RL) focuses on developing RL algorithms that guarantee safety during the learning process and deployment, addressing the inherent risk of unpredictable behavior in traditional RL. Current research emphasizes balancing reward maximization with constraint satisfaction, often employing techniques like gradient manipulation, control barrier functions, and Lagrangian methods within various model architectures, including actor-critic structures and Gaussian mixture policies. This field is crucial for enabling the safe application of RL in high-stakes domains such as robotics, autonomous driving, and power systems, where unexpected actions could have severe consequences. The development of provably safe and efficient algorithms remains a key focus, with ongoing efforts to improve sample efficiency and address challenges posed by non-stationary environments and multiple constraints.