294 citations · 1.2k across the 24 of their papers we have counts for
6 papers · 1 filter
On the importance of data collection for training general goal-reaching policies
Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold +3
Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist al…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold +3
Many real-world problems come with action spaces represented as feature vectors. Although high-dimensional control is a largely unsolved problem, there has recently been progress f…
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
Datum-Wise Classification: A Sequential Approach to Sparsity
Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux +1
We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation…
Text Classification: A Sequential Reading Approach
Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari
We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document…