7 citations · 7 across the 2 of their papers we have counts for
5 papers · 1 filter
Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights
Ken Ming Lee, Paul Barde, Maxime C. Cohen +1
Understanding customer movement within retail spaces is essential for optimizing store layouts. Real-world trajectory data can provide highly accurate insights, but collecting it i…
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
Paul Barde, Jakob Foerster, Derek Nowrouzezahrai +1
Training multiple agents to coordinate is an essential problem with applications in robotics, game theory, economics, and social sciences. However, most existing Multi-Agent Reinfo…
Regularized Inverse Reinforcement Learning
Wonseok Jeon, Chen-Yang Su, Paul Barde +3
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions. Regularize…
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…
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…