Learning how to select an action: A computational model

Abstract

Neurophysiological experimental results suggest that basal ganglia plays crucial role in action selection while dopamine modifies this process. There are computational models based on these experimental results for action selection. This work focuses on modification of action selection by dopamine release. In the model, a dynamical system is considered for action selection and modification of action selection process is realized by reinforcement learning. The ability of the proposed dynamical system is investigated by bifurcation analysis. Based on the results of this bifurcation analysis, the effect of reinforcement learning on action selection is discussed. The model is implemented on a mobile robot and a foraging task is realized where an exploration in an unfamiliar environment with training in the world is accomplished. Thus, this work fulfills its aim of showing the efficiency of brain-inspired computational models in controlling intelligent agents. © 2012 Springer-Verlag.