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Found 21 papers, 12 papers with code
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms
In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer.
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality.
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 of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence.
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 the return in the objective function in RL upside down, by taking returns as input and predicting actions.
Learning Relative Return Policies With Upside-Down Reinforcement Learning
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems.
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay
We evaluate DTGSH on five challenging robotic manipulation tasks in simulated robot environments, where we show that our method can learn more quickly and reach higher performance than other state-of-the-art approaches on all tasks.
A Pragmatic Look at Deep Imitation Learning
The introduction of the generative adversarial imitation learning (GAIL) algorithm has spurred the development of scalable imitation learning approaches using deep neural networks.
Privileged Information Dropout in Reinforcement Learning
Using privileged information during training can improve the sample efficiency and performance of machine learning systems.
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.
AlphaStar: An Evolutionary Computation Perspective
In January 2019, DeepMind revealed AlphaStar to the world-the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II-representing a milestone in the progress of AI.
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
Variational Inference for Data-Efficient Model Learning in POMDPs
In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference.
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.
On denoising autoencoders trained to minimise binary cross-entropy
When training autoencoders on image data a natural choice of loss function is BCE, since pixel values may be normalised to take values in [0, 1] and the decoder model may be designed to generate samples that take values in (0, 1).
A Brief Survey of Deep Reinforcement Learning
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
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 generative models.
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Improving Sampling from Generative Autoencoders with Markov Chains
Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model.
Towards Deep Symbolic Reinforcement Learning
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 demonstrated on tasks such as Atari video games and the game of Go.
Classifying Options for Deep Reinforcement Learning
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 policy network, and a supervisory network for choosing between the different options.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
