Work

Active learning and decision making: an introduction to the collection

Year: 2015

Type: article

Abstract: The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a wide range of disciplines ranging from machine and robot learning to neuroscience and psychology. Given the complexity of these behaviors, progress in understanding them will require a confluence of ideas from these multiple fields. This collection of articles in F1000Research aims to provide a home for a broad range of studies addressing this topic, including full length research articles, brief communications, single figure studies, and review/opinion articles, and studies using computational, behavioral or neural approaches. Here, we provide an introduction to the collection which we hope will grow and become a valuable resource for the researchers exploring this topic.

Source: F1000Research

DOI: 10.12688/f1000research.5757.2

Authors: Jacqueline Gottlieb, Manuel Lopes, Pierre-Yves Oudeyer

Institutions: Columbia University, Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'université de Bordeaux

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,350

OpenAlex ID: W1999984131

FWCI:

Cites: 7

Cited by: 1

Related to: 10