66 citations · 80 across the 10 of their papers we have counts for
6 papers · 1 filter
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.…
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…