Search Results for author: Marc-Alexandre Côté

Found 18 papers, 15 papers with code

Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language

no code implementations12 May 2022 Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander G. Schwing

In order to study how agents can be taught to query external knowledge via language, we first introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld.

ScienceWorld: Is your Agent Smarter than a 5th Grader?

1 code implementation14 Mar 2022 Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu

This paper presents a new benchmark, ScienceWorld, to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.

Question Answering

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

1 code implementation8 Oct 2020 Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, Matthew Hausknecht

ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.

Natural Language Visual Grounding Scene Understanding

Graph Policy Network for Transferable Active Learning on Graphs

1 code implementation NeurIPS 2020 Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang

Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields.

Active Learning

Interactive Fiction Games: A Colossal Adventure

2 code implementations11 Sep 2019 Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, Xingdi Yuan

A hallmark of human intelligence is the ability to understand and communicate with language.

Unsupervised State Representation Learning in Atari

3 code implementations NeurIPS 2019 Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.

Atari Games Representation Learning

Towards Solving Text-based Games by Producing Adaptive Action Spaces

2 code implementations3 Dec 2018 Ruo Yu Tao, Marc-Alexandre Côté, Xingdi Yuan, Layla El Asri

To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.

reinforcement-learning text-based games

Lessons learned in multilingual grounded language learning

1 code implementation CONLL 2018 Ákos Kádár, Desmond Elliott, Marc-Alexandre Côté, Grzegorz Chrupała, Afra Alishahi

Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language.

Grounded language learning

Revisiting the Hierarchical Multiscale LSTM

no code implementations COLING 2018 Ákos Kádár, Marc-Alexandre Côté, Grzegorz Chrupała, Afra Alishahi

Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input.

Language Modelling

Counting to Explore and Generalize in Text-based Games

2 code implementations29 Jun 2018 Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler

We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.

text-based games

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Neural Autoregressive Distribution Estimation

3 code implementations7 May 2016 Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle

We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation.

Density Estimation Image Generation

An Infinite Restricted Boltzmann Machine

1 code implementation9 Feb 2015 Marc-Alexandre Côté, Hugo Larochelle

We present a mathematical construction for the restricted Boltzmann machine (RBM) that doesn't require specifying the number of hidden units.

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