3 papers
cs.LG2026
Human-like autonomy emerges from self-play and a pinch of human data
Daphne Cornelisse, Julian Hunt, Zixu Zhang +4
Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data. It uses cheap, large-scale simulations to substitute expensive, lar…
cs.LG2025
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…
cs.LG2025
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…