Motivated Learning from Interesting Events: Adaptive, Multitask Learning Agents for Complex Environments

Abstract

This article presents a computational model of motivation for learning agents to achieve adaptive, multitask learning in complex, dynamic environments. Motivation is modeled as an attention focus mechanism to extend existing learning algorithms to environments in which tasks cannot be completely predicted prior to learning. Two agent models are presented for motivated reinforcement learning and motivated supervised learning, which incorporate this model of motivation. The formalisms used to define these agent models further allow the definition of consistent metrics for evaluating motivated learning agent models. The article concludes with a demonstration of the motivated reinforcement learning agent model that uses novelty and interest as the motivation function. The model is evaluated using the new metrics. Results show that motivated reinforcement learning agents using general, task-independent concepts such as novelty and interest can learn multiple, task-oriented behaviors by adapting their focus of attention in response to their changing experiences in their environment.