Rewards and errors in multi-arm bandits ...
Type de document :
Communication dans un congrès avec actes
Titre :
Rewards and errors in multi-arm bandits for interactive education
Auteur(s) :
Erraqabi, Akram [Auteur]
Université de Montréal [UdeM]
Sequential Learning [SEQUEL]
Lazaric, Alessandro [Auteur]
Sequential Learning [SEQUEL]
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
Brunskill, Emma [Auteur]
Computer Science Department - Carnegie Mellon University
Liu, Yun-En [Auteur]
Computer Science Department - Carnegie Mellon University
Université de Montréal [UdeM]
Sequential Learning [SEQUEL]
Lazaric, Alessandro [Auteur]

Sequential Learning [SEQUEL]
Valko, Michal [Auteur]

Sequential Learning [SEQUEL]
Brunskill, Emma [Auteur]
Computer Science Department - Carnegie Mellon University
Liu, Yun-En [Auteur]
Computer Science Department - Carnegie Mellon University
Titre de la manifestation scientifique :
Challenges in Machine Learning: Gaming and Education workshop at Neural Information Processing Systems
Ville :
Barcelona
Pays :
Espagne
Date de début de la manifestation scientifique :
2016
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
In multi-armed bandits, the most common objective is the maximization of the cumulative reward. Alternative settings include active exploration, where a learner tries to gain accurate estimates of the rewards of all arms. ...
Lire la suite >In multi-armed bandits, the most common objective is the maximization of the cumulative reward. Alternative settings include active exploration, where a learner tries to gain accurate estimates of the rewards of all arms. While these objectives are contrasting, in many scenarios it is desirable to trade off rewards and errors. For instance, in educational games the designer wants to gather generalizable knowledge about the behavior of the students and teaching strategies (small estimation errors) but, at the same time, the system needs to avoid giving a bad experience to the players, who may leave the system permanently (large reward). In this paper, we formalize this tradeoff and introduce the ForcingBalance algorithm whose performance is provably close to the best possible tradeoff strategy. Finally, we demonstrate on real-world educational data that ForcingBalance returns useful information about the arms without compromising the overall reward.Lire moins >
Lire la suite >In multi-armed bandits, the most common objective is the maximization of the cumulative reward. Alternative settings include active exploration, where a learner tries to gain accurate estimates of the rewards of all arms. While these objectives are contrasting, in many scenarios it is desirable to trade off rewards and errors. For instance, in educational games the designer wants to gather generalizable knowledge about the behavior of the students and teaching strategies (small estimation errors) but, at the same time, the system needs to avoid giving a bad experience to the players, who may leave the system permanently (large reward). In this paper, we formalize this tradeoff and introduce the ForcingBalance algorithm whose performance is provably close to the best possible tradeoff strategy. Finally, we demonstrate on real-world educational data that ForcingBalance returns useful information about the arms without compromising the overall reward.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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