Attentional Shifting and Curiosity: a Reinforcement Learning Approach

Título: Attentional Shifting and Curiosity: a Reinforcement Learning Approach

Autores: Nocera, Dario di; Finzi, Alberto; Rossi, Silvia; Staffa, Mariacarla

Resumo: Attention allocation strategies represent key capabilities of human beings, which are strictly connected with action selection and execution mechanisms, while intrinsic motivations directly affect the allocation of attentional resources. In this paper we propose a model of Reinforcement Learning (RL), where both these capabilities are involved. RL is deployed to learn how to allocate attentional resources in a behavior-based robotic system, while action selection is obtained as a side effect of the resulting motivated attentional behaviors. Moreover, the influence of intrinsic motivations in attention orientation is obtained by introducing internal rewards associated with curiosity drives. In this way, the learning process is affected not only by goalspecific rewards, but also by intrinsic motivations depending on the internal state of the system.

Palavras-chave:

Páginas: 6

Código DOI: 10.21528/CBIC2013-256

Artigo em pdf: bricsccicbic2013_submission_256.pdf

Arquivo BibTex: bricsccicbic2013_submission_256.bib