Marie‐Paule Cani

Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramér Surrogate

2025 · ArXiv.org

Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard approaches often remain far from parsimonious. Categorical methods (e.g., C51) rely on fixed supports where parameter counts scale linearly with …