Safe Reinforcement Learning via Probabilistic Shields

Understand

This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty.

• Markov decision processes (MDPs) are prominent models to capture such planning problems.
• Reinforcement learning (RL) is a machine learning technique to determine near-optimal policies in MDPs that may be unknown prior to exploring the model.
• However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts.