Robust Safe Reinforcement Learning under Adversarial Disturbances

Understand

Safety is a primary concern when applying reinforcement learning to real-world control tasks, especially in the presence of external disturbances.

• However, existing safe reinforcement learning algorithms rarely account for external disturbances, limiting their applicability and robustness in practice.
• To address this challenge, this paper proposes a robust safe reinforcement learning framework that tackles worst-case disturbances.
• First, this paper presents a policy iteration scheme to solve for the robust invariant set, i.e., a subset of the safe set, where persistent safety is only possible for states within.