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. © 2010 Springer-Verlag.
CITATION STYLE
Hirel, J., Gaussier, P., Quoy, M., & Banquet, J. P. (2010). Why and how hippocampal transition cells can be used in reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6226 LNAI, pp. 359–369). https://doi.org/10.1007/978-3-642-15193-4_34
Mendeley helps you to discover research relevant for your work.