66 citations · 80 across the 10 of their papers we have counts for
13 papers
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers +6
We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly created with a minimalistic design para…
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop
Guillaume Alain, Maxime Chevalier-Boisvert, Frederic Osterrath +1
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions.…
BabyAI 1.1
David Yu-Tung Hui, Maxime Chevalier-Boisvert, Dzmitry Bahdanau +1
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 presents baseline results of an agent tra…
Combating False Negatives in Adversarial Imitation Learning
Konrad Zolna, Chitwan Saharia, Leonard Boussioux +4
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior. However, as the trained…
Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert +2
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. The options framework describes such behav…
Automated curriculum generation for Policy Gradients from Demonstrations
Anirudh Srinivasan, Dzmitry Bahdanau, Maxime Chevalier-Boisvert +1
In this paper, we present a technique that improves the process of training an agent (using RL) for instruction following. We develop a training curriculum that uses a nominal numb…
Robo-PlaNet: Learning to Poke in a Day
Maxime Chevalier-Boisvert, Guillaume Alain, Florian Golemo +1
Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observation…
Option-Critic in Cooperative Multi-agent Systems
Jhelum Chakravorty, Nadeem Ward, Julien Roy +4
In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999). First, we address the planning pr…
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou +4
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficie…
Interprocedural Type Specialization of JavaScript Programs Without Type Analysis
Maxime Chevalier-Boisvert, Marc Feeley
Dynamically typed programming languages such as Python and JavaScript defer type checking to run time. VM implementations can improve performance by eliminating redundant dynamic t…
Extending Basic Block Versioning with Typed Object Shapes
Maxime Chevalier-Boisvert, Marc Feeley
Typical JavaScript (JS) programs feature a large number of object property accesses. Hence, fast property reads and writes are crucial for good performance. Unfortunately, many (of…
Simple and Effective Type Check Removal through Lazy Basic Block Versioning
Maxime Chevalier-Boisvert, Marc Feeley
Dynamically typed programming languages such as JavaScript and Python defer type checking to run time. In order to maximize performance, dynamic language VM implementations must at…
Removing Dynamic Type Tests with Context-Driven Basic Block Versioning
Maxime Chevalier-Boisvert, Marc Feeley
Dynamic typing is an important feature of dynamic programming languages. Primitive operators such as those for performing arithmetic and comparisons typically operate on a wide var…