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Social interacting is a complex task for which machine learning holds particular promise. However, as no sufficiently accurate simulator of human interactions exists today, the learning of social interaction strategies has to happen online in the real world. Actions executed by the robot impact on humans, and as such have to be carefully selected, making it impossible to rely on random exploration. Additionally, no clear reward function exists for social interactions. This implies that traditional approaches used for Reinforcement Learning cannot be directly applied for learning how to interact with the social world. As such we argue that robots will profit from human expertise and guidance to learn social interactions. However, as the quantity of input a human can provide is limited, new methods have to be designed to use human input more efficiently. In this paper we describe a setup in which we combine a framework called Supervised Progressively Autonomous Robot Competencies (SPARC), which allows safer online learning with Reinforcement Learning, with the use of partial states rather than full states to accelerate generalisation and obtain a usable action policy more quickly.

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Faculty of Technology ePrints Service  


Senft, Emmanuel -  Lemaignan, Severin -  Baxter, Paul -  Belpaeme, Tony - 

Id.: 70961932

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveG400 Computer Science - 

Tipo de recurso: Conference or Workshop contribution  -  NonPeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://eprints.lincoln.ac.uk/30193/
[References] https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/16011/15296

Fecha de contribución: 04-feb-2018


* Senft, Emmanuel and Lemaignan, Severin and Baxter, Paul and Belpaeme, Tony (2017) Toward supervised reinforcement learning with partial states for social HRI. In: 4th AAAI FSS on Artificial Intelligence for Social Human-Robot Interaction (AI-HRI), 9 - 11 November 2017, Arlington, Virginia.

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