570 citations · 2.6k across the 75 of their papers we have counts for
7 papers · 1 filter
LIG-CRIStAL System for the WMT17 Automatic Post-Editing Task
Alexandre Berard, Olivier Pietquin, Laurent Besacier
This paper presents the LIG-CRIStAL submission to the shared Automatic Post- Editing task of WMT 2017. We propose two neural post-editing models: a monosource model with a task-spe…
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Mel Vecerik, Todd Hester, Jonathan Scholz +7
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algor…
Modulating early visual processing by language
Harm de Vries, Florian Strub, Jérémie Mary +3
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view dominates the current literature in comput…
Observational Learning by Reinforcement Learning
Diana Borsa, Bilal Piot, Rémi Munos +1
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core me…
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to…
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…
End-to-end optimization of goal-driven and visually grounded dialogue systems
Florian Strub, Harm de Vries, Jeremie Mary +3
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.…