19 citations · 20 across the 8 of their papers we have counts for
8 papers
Multiple-Frequencies Population-Based Training
Waël Doulazmi, Auguste Lehuger, Marin Toromanoff +3
Reinforcement Learning's high sensitivity to hyperparameters is a source of instability and inefficiency, creating significant challenges for practitioners. Hyperparameter Optimiza…
Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Raphael Chekroun, Han Wang, Jonathan Lee +4
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to m…
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Raphael Chekroun, Marin Toromanoff, Sascha Hornauer +1
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is no…
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…