Zissis Poulos

A Robust Quantile Huber Loss with Interpretable Parameter Adjustment in Distributional Reinforcement Learning

Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold parameter often selected heuristically or via hyperparameter search, which may not generalize well and can be suboptimal.…