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Algorithmes de sortie du Piège de la Zone Ennuyeuse en apprentissage par renforcement

Abstract : Reinforcement learning algorithms have succeeded over the years in achieving impressive results in a variety of fields. However, these algorithms suffer from certain weaknesses highlighted by Refael Vivanti and al. that may explain the regression of even well-trained agents in certain environments : the difference in variance on rewards between areas of the environment. This difference in variance leads to two problems : Boring Area Trap and Manipulative consultant. We note that the Adaptive Symmetric Reward Noising (ASRN) algorithm proposed by Refael Vivanti and al. has limitations for environments with the following characteristics : long game times and multiple boring area environments. To overcome these problems, we propose three algorithms derived from the ASRN algorithm called Rebooted Adaptive Symmetric Reward Noising (RASRN) : Continuous ε decay RASRN, Full RASRN and Stepwise α decay RASRN. Thanks to two series of experiments carried out on the k-armed bandit problem, we show that our algorithms can better correct the Boring Area Trap problem.
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Contributor : Landry Steve Noulawe Tchamanbe Connect in order to contact the contributor
Submitted on : Monday, August 31, 2020 - 5:54:16 PM
Last modification on : Tuesday, December 7, 2021 - 5:50:03 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 12:53:08 PM

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Landry Steve Noulawe Tchamanbe, Paulin Melatagia Yonta. Algorithmes de sortie du Piège de la Zone Ennuyeuse en apprentissage par renforcement. CARI 2020 - Colloque Africain sur la Recherche en Informatique et Mathématiques Appliquées, Oct 2020, Thiès, Sénégal. ⟨hal-02926408⟩

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