Lyapunov-based uncertainty-aware safe reinforcement learning

Understand

Reinforcement learning (RL) has shown a promising performance in learning optimal policies for a variety of sequential decision-making tasks.

• However, in many real-world RL problems, besides optimizing the main objectives, the agent is expected to satisfy a certain level of safety (e.g., avoiding collisions in autonomous driving).
• While RL problems are commonly formalized as Markov decision processes (MDPs), safety constraints are incorporated via constrained Markov decision processes (CMDPs).
• Although recent advances in safe RL have enabled learning safe policies in CMDPs, these safety requirements should be satisfied during both training and in the deployment process.