Safe Reinforcement Learning via Shielding under Partial Observability

Understand

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment.

• A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action.
• A so-called shield forces the RL agent to select only safe actions.
• However, for adoption in various applications, one must look beyond enforcing safety and also ensure the applicability of RL with good performance.