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10/25/2022
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms
In mixed-initiative co-creation tasks, where a human and a machine joint...
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05/12/2022
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
When generating content for video games using procedural content generat...
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03/24/2022
On the link between conscious function and general intelligence in humans and machines
In popular media, there is often a connection drawn between the advent o...
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02/24/2022
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
Upside down reinforcement learning (UDRL) flips the conventional use of ...
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02/23/2022
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Lately, there has been a resurgence of interest in using supervised lear...
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08/17/2021
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay
Hindsight experience replay (HER) is a goal relabelling technique typica...
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08/04/2021
A Pragmatic Look at Deep Imitation Learning
The introduction of the generative adversarial imitation learning (GAIL)...
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05/19/2020
Privileged Information Dropout in Reinforcement Learning
Using privileged information during training can improve the sample effi...
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12/18/2019
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Deep reinforcement learning has the potential to train robots to perform...
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11/21/2019
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Deep networks have enabled reinforcement learning to scale to more compl...
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11/21/2019
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Recently, neuro-inspired episodic control (EC) methods have been develop...
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02/05/2019
AlphaStar: An Evolutionary Computation Perspective
In January 2019, DeepMind revealed AlphaStar to the world-the first arti...
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07/17/2018
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate para...
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05/23/2018
Variational Inference for Data-Efficient Model Learning in POMDPs
Partially observable Markov decision processes (POMDPs) are a powerful a...
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10/19/2017
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep repre...
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08/28/2017
On denoising autoencoders trained to minimise binary cross-entropy
Denoising autoencoders (DAEs) are powerful deep learning models used for...
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08/19/2017
A Brief Survey of Deep Reinforcement Learning
Deep reinforcement learning is poised to revolutionise the field of AI a...
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11/08/2016
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gau...
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10/28/2016
Improving Sampling from Generative Autoencoders with Markov Chains
We focus on generative autoencoders, such as variational or adversarial ...
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09/18/2016
Towards Deep Symbolic Reinforcement Learning
Deep reinforcement learning (DRL) brings the power of deep neural networ...
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04/27/2016
