Distinction between types of motivations: Emergent behavior with a neural, model-based reinforcement learning system

Abstract

In this paper, we analyze the behavior of a simulated mobile robot, which interacts with an initially unknown maze-environment. The robot is controlled by an interactive system that is based on a model building Time Growing Neural Gas (TGNG) algorithm and a homeostatic motivational system, which activates movement preferences and goals within the emergent model structure for behavioral control. We propose to differentiate two types of drives (if not more), which we call location- and characteristics-based drives. We exemplary implement the two types of drives by “hunger” and “fear”, respectively. Several possible methods of combination of the two drives are investigated through simulation, identifying the combination that lead to the most suitable emergent behavior, such as emergent “wall-following” and “hiding”. Moreover, we investigate performance in an ALife-like scenario, in which the robot interacts with several food-dispensers. It is shown that additional behavioral concepts, such as “curiosity” and “inhibition of return”, can maximize the survival chances of the organism, who maintains maximal safety and keeps its belly full. In conclusion, we propose that the concept of motivation needs to be further differentiated to realize autonomous, life-like robots that are able to optimally satisfy multiple, competing types of motivations by emergent, innovative behavioral patterns.