14 citations · 31 across the 7 of their papers we have counts for
7 papers
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…
Learning to Guide and to Be Guided in the Architect-Builder Problem
Paul Barde, Tristan Karch, Derek Nowrouzezahrai +3
We are interested in interactive agents that learn to coordinate, namely, a -- which performs actions but ignores the goal of the task, i.e. has no access to rewards -- a…
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…
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
Wonseok Jeon, Paul Barde, Derek Nowrouzezahrai +1
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies…