Shaping as a method for accelerating reinforcement learning

Learning complex control behavior by building some initial control knowledge into the learning controller through shaping is addressed. The principle underlying shaping is that learning to solve complex problems can be facilitated by first learning to solve related simpler problems. The authors present experimental results illustrating the utility of shaping in training controllers by means of reinforcement learning methods. Shaping a reinforcement learning controller´s behavior over time by gradually increasing the complexity of the control task as the controller learns makes it possible to scale reinforcement learning methods to more complex tasks. This is illustrated by an example