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Importance Weighted Transfer of Samples ...
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Document type :
Communication dans un congrès avec actes
Title :
Importance Weighted Transfer of Samples in Reinforcement Learning
Author(s) :
Tirinzoni, Andrea [Auteur]
Sessa, Andrea [Auteur]
Pirotta, Matteo [Auteur]
Sequential Learning [SEQUEL]
Restelli, Marcello [Auteur]
Conference title :
ICML 2018 - The 35th International Conference on Machine Learning
City :
Stockholm
Country :
Suède
Start date of the conference :
2018-07-10
Journal title :
Proceedings of Machine Learning Research
HAL domain(s) :
Statistiques [stat]/Machine Learning [stat.ML]
English abstract : [en]
We consider the transfer of experience samples (i.e., tuples < s, a, s', r >) in reinforcement learning (RL), collected from a set of source tasks to improve the learning process in a given target task. Most of the related ...
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We consider the transfer of experience samples (i.e., tuples < s, a, s', r >) in reinforcement learning (RL), collected from a set of source tasks to improve the learning process in a given target task. Most of the related approaches focus on selecting the most relevant source samples for solving the target task, but then all the transferred samples are used without considering anymore the discrepancies between the task models. In this paper, we propose a model-based technique that automatically estimates the relevance (importance weight) of each source sample for solving the target task. In the proposed approach, all the samples are transferred and used by a batch RL algorithm to solve the target task, but their contribution to the learning process is proportional to their importance weight. By extending the results for importance weighting provided in supervised learning literature, we develop a finite-sample analysis of the proposed batch RL algorithm. Furthermore, we empirically compare the proposed algorithm to state-of-the-art approaches, showing that it achieves better learning performance and is very robust to negative transfer, even when some source tasks are significantly different from the target task.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Extraction et transfert de connaissances dans l'apprentissage par renforcement
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
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