1 code implementation • 5 Jul 2023 • Stephanie Long, Alexandre Piché, Valentina Zantedeschi, Tibor Schuster, Alexandre Drouin
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making.
no code implementations • 7 Mar 2023 • Stephanie Long, Tibor Schuster, Alexandre Piché
Building causal graphs can be a laborious process.
1 code implementation • 24 Sep 2022 • Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste
In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks.
1 code implementation • 4 Jun 2021 • Alexandre Piché, Valentin Thomas, Rafael Pardinas, Joseph Marino, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
1 code implementation • NeurIPS 2021 • Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.
1 code implementation • 9 May 2018 • Joshua Romoff, Peter Henderson, Alexandre Piché, Vincent Francois-Lavet, Joelle Pineau
However, introduction of corrupt or stochastic rewards can yield high variance in learning.
no code implementations • 22 Dec 2017 • Rémi Le Priol, Alexandre Piché, Simon Lacoste-Julien
In this paper, we adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform sampling strategy based on block duality gaps.
no code implementations • 24 Oct 2016 • Alexandre Piché, Russell Steele, Ian Shrier, Stephanie Long
Datasets containing large samples of time-to-event data arising from several small heterogeneous groups are commonly encountered in statistics.