7 citations · 21 across the 6 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 for Autonomous Driving: A Survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert +4
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in…
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Victor Talpaert, Ibrahim Sobh, B Ravi Kiran +4
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in comme…