4 papers
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
Marin Toromanoff, Emilie Wirbel, Fabien Moutarde
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable o…
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
Marin Toromanoff, Emilie Wirbel, Fabien Moutarde
Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameter…
End-to-End Race Driving with Deep Reinforcement Learning
Maximilian Jaritz, Raoul de Charette, Marin Toromanoff +2
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly pr…
End to End Vehicle Lateral Control Using a Single Fisheye Camera
Marin Toromanoff, Emilie Wirbel, Frédéric Wilhelm +3
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failur…