Open-Ended Learning: A Conceptual Framework Based on Representational Redescription-Reference-Cited by-同舟云学术

Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

Published:2018-09-25 Issue: Volume:12 Page:
ISSN:1662-5218
Container-title:Frontiers in Neurorobotics
language:
Short-container-title:Front. Neurorobot.

Author:

Doncieux Stephane,Filliat David,Díaz-Rodríguez Natalia,Hospedales Timothy,Duro Richard,Coninx Alexandre,Roijers Diederik M.,Girard Benoît,Perrin Nicolas,Sigaud Olivier

Funder

H2020 Future and Emerging Technologies

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Biomedical Engineering

Reference23 articles.

1. Intrinsically Motivated Learning in Natural and Artificial Systems

2. Intrinsically motivated goal exploration for active motor learning in robots: a case study;Baranes,2010

3. Recent advances in hierarchical reinforcement learning;Barto;Discrete Event Syst.,2003

4. Curriculum learning;Bengio,2009

5. Autonomous learning of state representations for control: an emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations;Böhmer;KI-Künstliche Intell.,2015

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