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Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays

Abstract : During sleep and awake rest, the hippocampus replays sequences of place cells that have been activated during prior experiences. These have been interpreted as a memory consolidation process, but recent results suggest a possible interpretation in terms of reinforcement learning. The Dyna reinforcement learning algorithms use off-line replays to improve learning. Under limited replay budget, a prioritized sweeping approach, which requires a model of the transitions to the predecessors, can be used to improve performance. We investigate whether such algorithms can explain the experimentally observed replays. We propose a neural network version of prioritized sweeping Q-learning, for which we developed a growing multiple expert algorithm, able to cope with multiple predecessors. The resulting architecture is able to improve the learning of simulated agents confronted to a navigation task. We predict that, in animals, learning the world model should occur during rest periods, and that the corresponding replays should be shuffled.
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Contributor : Benoît Girard <>
Submitted on : Wednesday, February 14, 2018 - 5:56:05 PM
Last modification on : Wednesday, May 19, 2021 - 11:58:16 AM
Long-term archiving on: : Monday, May 7, 2018 - 1:10:46 PM


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Lise Aubin, Mehdi Khamassi, Benoît Girard. Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays. Biomimetic and Biohybrid Systems. Living Machines 2018., Jul 2018, Paris, France. pp.16-27, ⟨10.1007/978-3-319-95972-6_4⟩. ⟨hal-01709275⟩



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