Dynamic Adjustment of the Motivation Degree in an Action Selection Mechanism

TL;DR

This paper introduces a reinforcement learning-based model for dynamically adjusting motivation levels in an action selection mechanism, validated through VR experiments, emphasizing the importance of adaptive learning in decision-making systems.

Contribution

It proposes a novel reinforcement learning method for real-time motivation adjustment in action selection models, demonstrated via VR simulations.

Findings

Effective dynamic motivation adjustment demonstrated in VR

Learning improves action selection adaptability

Model shows potential for adaptive decision-making systems

Abstract

This paper presents a model for dynamic adjustment of the motivation degree, using a reinforcement learning approach, in an action selection mechanism previously developed by the authors. The learning takes place in the modification of a parameter of the model of combination of internal and external stimuli. Experiments that show the claimed properties are presented, using a VR simulation developed for such purposes. The importance of adaptation by learning in action selection is also discussed.