Portrait de Paul Barde

Paul Barde

paul.b.barde@gmail.com
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e
Derek Nowrouzezahrai
Co-supervisor
Chris Pal
Sujets de recherche
Apprentissage par renforcement
Site web
Google Scholar
GitHub

Publications

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
Paul Barde
Jakob Nicolaus Foerster
Derek Nowrouzezahrai
Amy Zhang
2024-01-01
AAMAS (publié)
doi.org
arxiv.org
From Words to Blocks: Building Objects by Grounding Language Models with Reinforcement Learning
Michael Ahn
Anthony Brohan
Noah Brown
liang Dai
Dan Su
Holy Lovenia Ziwei Bryan Wilie
Tiezheng Yu
Willy Chung
Quyet V. Do
Paul Barde
Tristan Karch
Derek Nowrouzezahrai
C. Bonial
Mitchell Abrams
David R. Traum
Hyung Won
Le Hou
Shayne Longpre
Yi Zoph
William Tay … (voir 32 de plus)
Eric Fedus
Xuezhi Li
Lasse Espeholt
Hubert Soyer
Remi Munos
Karen Si-801
Vlad Mnih
Tom Ward
Yotam Doron
Wenlong Huang
Pieter Abbeel
Deepak Pathak
Julia Kiseleva
Ziming Li
Mohammad Aliannejadi
Shrestha Mohanty
Maartje Ter Hoeve
Mikhail Burtsev
Alexey Skrynnik
Artem Zholus
A. Panov
Kavya Srinet
A. Szlam
Yuxuan Sun
Katja Hofmann
Marc-Alexandre Côté
Ahmed Hamid Awadallah
Linar Abdrazakov
Igor Churin
Putra Manggala
Kata Naszádi
Michiel Van Der Meer
Leveraging pre-trained language models to gen-001 erate action plans for embodied agents is an 002 emerging research direction. However, exe… (voir plus)-003 cuting instructions in real or simulated envi-004 ronments necessitates verifying the feasibility 005 of actions and their relevance in achieving a 006 goal. We introduce a novel method that in-007 tegrates a language model and reinforcement 008 learning for constructing objects in a Minecraft-009 like environment, based on natural language 010 instructions. Our method generates a set of 011 consistently achievable sub-goals derived from 012 the instructions and subsequently completes the 013 associated sub-tasks using a pre-trained RL pol-014 icy. We employ the IGLU competition, which 015 is based on the Minecraft-like simulator, as our 016 test environment, and compare our approach 017 to the competition’s top-performing solutions. 018 Our approach outperforms existing solutions in 019 terms of both the quality of the language model 020 and the quality of the structures built within the 021 IGLU environment. 022
Learning to Guide and to Be Guided in the Architect-Builder Problem
Paul Barde
Tristan Karch
Derek Nowrouzezahrai
Clément Moulin-Frier
Chris Pal
Pierre-Yves Oudeyer
We are interested in interactive agents that learn to coordinate, namely, a …
2022-01-28
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde
Julien Roy
Wonseok Jeon
Joelle Pineau
Chris Pal
Derek Nowrouzezahrai
Adversarial imitation learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones … (voir plus)-- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of adversarial imitation learning algorithms by removing the reinforcement learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent imitation learning methods.
arxiv.org