A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints

Understand

In many applications of Reinforcement Learning (RL), it is critically important that the algorithm performs safely, such that instantaneous hard constraints are satisfied at each step, and unsafe states and actions are avoided.

• However, existing algorithms for ''safe'' RL are often designed under constraints that either require expected cumulative costs to be bounded or assume all states are safe.
• Thus, such algorithms could violate instantaneous hard constraints and traverse unsafe states (and actions) in practice.
• Therefore, in this paper, we develop the first near-optimal safe RL algorithm for episodic Markov Decision Processes with unsafe states and actions under instantaneous hard constraints and the linear mixture model.