RT Journal Article
SR Electronic
T1 Experience resetting in reinforcement learning facilitates exploration–exploitation transitions during a behavioral task for primates
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.30.462676
DO 10.1101/2021.09.30.462676
A1 Sakamoto, Kazuhiro
A1 Okuzaki, Hidetake
A1 Sato, Akinori
A1 Mushiake, Hajime
YR 2021
UL http://biorxiv.org/content/early/2021/10/01/2021.09.30.462676.abstract
AB The exploration–exploitation trade-off is a fundamental problem in re-inforcement learning. To study the neural mechanisms involved in this problem, a target search task in which exploration and exploitation phases appear alternately is useful. Monkeys well trained in this task clearly understand that they have entered the exploratory phase and quickly acquire new experiences by resetting their previous experiences. In this study, we used a simple model to show that experience resetting in the exploratory phase improves performance rather than decreasing the greediness of action selection, and we then present a neural network-type model enabling experience resetting.Competing Interest StatementThe authors have declared no competing interest.