Student-Initiated Action Advising via Advice Novelty

Understand

Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL).

• Most recently, student-initiated techniques that utilise state novelty and uncertainty estimations have obtained promising results.
• However, the approaches built on these estimations have some potential weaknesses.
• First, they assume that the convergence of the student's RL model implies less need for advice.