1.6k citations · 8k across the 93 of their papers we have counts for
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An Introduction to Deep Reinforcement Learning
Vincent Francois-Lavet, Peter Henderson, Riashat Islam +2
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-maki…
Deep Generative Modeling of LiDAR Data
Lucas Caccia, Herke van Hoof, Aaron Courville +1
Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has be…
Natural Environment Benchmarks for Reinforcement Learning
Amy Zhang, Yuxin Wu, Joelle Pineau
While current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data.…
Compositional Language Understanding with Text-based Relational Reasoning
Koustuv Sinha, Shagun Sodhani, William L. Hamilton +1
Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference. However, it is also crucial to und…
The RLLChatbot: a solution to the ConvAI challenge
Nicolas Gontier, Koustuv Sinha, Peter Henderson +4
Current conversational systems can follow simple commands and answer basic questions, but they have difficulty maintaining coherent and open-ended conversations about specific topi…
Adversarial Gain
Peter Henderson, Koustuv Sinha, Rosemary Nan Ke +1
Adversarial examples can be defined as inputs to a model which induce a mistake - where the model output is different than that of an oracle, perhaps in surprising or malicious way…
Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus +3
Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Likelihood (MLE) models trained with teache…
Temporal Regularization in Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau +1
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly…
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
Peter Henderson, Joshua Romoff, Joelle Pineau
Recent analyses of certain gradient descent optimization methods have shown that performance can degrade in some settings - such as with stochasticity or implicit momentum. In deep…
TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff +4
We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing…
Extending Neural Generative Conversational Model using External Knowledge Sources
Prasanna Parthasarathi, Joelle Pineau
The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often…
Sequential Coordination of Deep Models for Learning Visual Arithmetic
Eric Crawford, Guillaume Rabusseau, Joelle Pineau
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipul…
Combined Reinforcement Learning via Abstract Representations
Vincent François-Lavet, Yoshua Bengio, Doina Precup +1
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly…
On-line Adaptative Curriculum Learning for GANs
Thang Doan, Joao Monteiro, Isabela Albuquerque +4
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence…
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Iulian Vlad Serban, Chinnadhurai Sankar, Michael Pieper +2
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-eff…
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
Amy Zhang, Nicolas Ballas, Joelle Pineau
The risks and perils of overfitting in machine learning are well known. However most of the treatment of this, including diagnostic tools and remedies, was developed for the superv…
Focused Hierarchical RNNs for Conditional Sequence Processing
Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni +6
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most of these models use a simple form of enco…
Randomized Value Functions via Multiplicative Normalizing Flows
Ahmed Touati, Harsh Satija, Joshua Romoff +2
Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike t…
Decoupling Dynamics and Reward for Transfer Learning
Amy Zhang, Harsh Satija, Joelle Pineau
Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this wo…
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
Joshua Romoff, Peter Henderson, Alexandre Piché +2
Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high varia…
Disentangling the independently controllable factors of variation by interacting with the world
Valentin Thomas, Emmanuel Bengio, William Fedus +6
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of traini…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…