NFDRL models return distributions via continuous normalizing flows paired with a geometry-aware Cramér surrogate distance, delivering fixed-size parameters, a sqrt(gamma) contraction, unbiased gradients, and competitive Atari-5 performance.
Revisitingthearcadelearningenvironment: Evaluationprotocolsandopen problems for general agents.Journal of Artificial Intelligence Research, 61:523–562
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Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cram\'er Surrogate
NFDRL models return distributions via continuous normalizing flows paired with a geometry-aware Cramér surrogate distance, delivering fixed-size parameters, a sqrt(gamma) contraction, unbiased gradients, and competitive Atari-5 performance.