Why and How Hippocampal Transition Cells Can Be Used in Reinforcement Learning View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Julien Hirel , Philippe Gaussier , Mathias Quoy , Jean-Paul Banquet

ABSTRACT

In this paper we present a model of reinforcement learning (RL) which can be used to solve goal-oriented navigation tasks. Our model supposes that transitions between places are learned in the hippocampus (CA pyramidal cells) and associated with information coming from path-integration. The RL neural network acts as a bias on these transitions to perform action selection. RL originates in the basal ganglia and matches observations of reward-based activity in dopaminergic neurons. Experiments were conducted in a simulated environment. We show that our model using transitions and inspired by Q-learning performs more efficiently than traditional actor-critic models of the basal ganglia based on temporal difference (TD) learning and using static states. More... »

PAGES

359-369

Book

TITLE

From Animals to Animats 11

ISBN

978-3-642-15192-7
978-3-642-15193-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15193-4_34

DOI

http://dx.doi.org/10.1007/978-3-642-15193-4_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018888212


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