3 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
V-Max: A Reinforcement Learning Framework for Autonomous Driving
Valentin Charraut, Waël Doulazmi, Thomas Tournaire +1
Learning-based decision-making has the potential to enable generalizable Autonomous Driving (AD) policies, reducing the engineering overhead of rule-based approaches. Imitation Lea…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…