| Téléchargement | - Voir la version finale : Biologically-based neural representations enable fast online shallow reinforcement learning (PDF, 398 Kio)
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| Auteur | Rechercher : Bartlett, Madeleine; Rechercher : Stewart, Terrence C.1; Rechercher : Orchard, Jeff |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022, July 27-30, 2022, Toronto, ON, Canada |
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| Sujet | grid cells; reinforcement learning; spatial semantic pointers |
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| Résumé | Biological brains learn much more quickly than standard deep neural network reinforcement learning algorithms. One reason for this is that the deep neural networks need to learn a representation that is appropriate for the task at hand, whilst biological systems already possess an appropriate representation. Here, we bypass this problem by imposing on the neural network a representation based on what is observed in biology, such as grid cells. This study explores the impact of using a biologically-inspired grid-cell representation vs. a one-hot representation, on the speed at which a Temporal Difference-based Actor-Critic network learns to solve a simple 2D gridworld reinforcement learning task. The results suggest that the use of grid cells does promote faster learning. Furthermore, the grid cells implemented here have the potential for accurately representing unbounded continuous space. Thus, their promising performance on this discrete task acts as a first step in exploring their utility for reinforcement learning in continuous space. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY) |
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| Date de publication | 2022-07-27 |
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| Maison d’édition | The Cognitive Science Society |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | c0ffc77c-d09b-4c7a-8b11-3d2b3b0fd0c2 |
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| Enregistrement créé | 2023-05-15 |
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| Enregistrement modifié | 2023-05-16 |
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