Sami Jullien

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression

Distributional reinforcement learning (RL) has proven useful in multiple benchmarks as it enables approximating the full distribution of returns and extracts a rich feedback from environment samples. The commonly used quantile regression approach to distributional RL – based on asymmetric $L_1$ loss