498 citations · 924 across the 7 of their papers we have counts for
4 papers · 1 filter
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
Maytus Piriyajitakonkij, Sirawaj Itthipuripat, Theerawit Wilaiprasitporn +1
Supervised deep convolutional neural networks (DCNNs) are currently one of the best computational models that can explain how the primate ventral visual stream solves object recogn…
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski +1
The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set…
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo +4
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep gene…
Classifying Options for Deep Reinforcement Learning
Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan +1
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the…