6 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
Mathieu Reymond, Conor F. Hayes, Lander Willem +8
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement l…
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers +2
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decision…
Deep Reinforcement Learning: An Overview
Seyed Sajad Mousavi, Michael Schukat, Enda Howley
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern re…
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Seyed Sajad Mousavi, Michael Schukat, Enda Howley
Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems wit…