Before revision:  In this paper, the novel Scaffolding Reflection in Reinforcement Learning ( SR2L ) framework is proposed, using a potential field method as a scaffold to assist the actor's action-reflection. Through the user's clearly articulated intent, the action-reflection enables the actor to learn by observing the probable actions and their values based on experience.
After revision:  In this paper, the novel Scaffolding Reflection in Reinforcement Learning ( SR2L ) framework is proposed, using a potential field method as a scaffold for the actor-critic network to observe, compare and learn the action-reward pair. It enables the evader to achieve the required objective while using lesser resources and time. Convergence studies show that SR2L learns faster and converges to higher rewards as compared to IAC.