Motivation in Embodied Intelligence
Artur Janusz · InTech eBooks · 2008
This chapter presented a goal creation system that motivates embodied intelligence to learn how to efficiently interact with the environment. The system uses artificial curiosity to explore, and the creation of abstract goals to learn efficiently and purposefully. It develops higher level abstract goals and increases the internal complexity of representations and skills that it stores in its memory. It was demonstrated that this type of system learns better and faster than traditional reinforcement learning systems. In a striking contrast to classical reinforcement learning, where the reinforcement signals come from the outside environment, GCS generates an internal reward associated with the abstract goal that the machine was able to accomplish. This makes the reinforcement process not observable, and to some degree makes the machine less controllable than one whose operation is based on classical reinforcement learning. The machine's actions are more difficult to understand and explain by an external observer, thus the machine behaves