activity
20192023
most citedPromoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

14 citations · 26 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2023★ 1 cited

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

Julien Roy, Pierre-Luc Bacon, Christopher Pal +1

In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound for pharmaceutical applications, there ar…

cs.LG2021★ 4 cited

Direct Behavior Specification via Constrained Reinforcement Learning

Julien Roy, Roger Girgis, Joshua Romoff +2

The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. Most often, practitioners go about the task of b…

cs.LG2020★ 7 cited

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Paul Barde, Julien Roy, Wonseok Jeon +3

Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce t…

cs.AI2019

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…

cs.LG2019★ 14 cited

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Julien Roy, Paul Barde, Félix G. Harvey +2

In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the nu…