498 citations · 660 across the 5 of their papers we have counts for
5 papers
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli +2
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An altern…
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico +2
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-par…
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…
Improving Sampling from Generative Autoencoders with Markov Chains
Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders a…
Towards Deep Symbolic Reinforcement Learning
Marta Garnelo, Kai Arulkumaran, Murray Shanahan
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demo…