[2605.08104] Distributional Reinforcement Learning via the Cramér Distance
[Submitted on 26 Apr 2026]
Title:Distributional Reinforcement Learning via the Cramér Distance
View a PDF of the paper titled Distributional Reinforcement Learning via the Cram\'er Distance, by Vanya Aziz and 2 other authors
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Abstract:This paper explores the application of the Soft Actor-Critic (SAC) algorithm within a Distributional Reinforcement Learning setting and introduces an implementation of such algorithm named Cramér-based Distributional Soft Actor-Critic (C-DSAC). The novel approach employs distributional reinforcement learning to represent state-action values, and minimizes the squared Cramér distance for learning the distribution. Empirical results across various robotic benchmarks indicate that our algorithm surpasses the performance of baseline SAC and contemporary distributional methods, with the performance advantage becoming increasingly pronounced in high-complexity environments. To explain the efficiency of the new approach, we conduct an analysis showing that its superior performance is partly due to \textit{confidence-driven} Q-value updates: High-variance target distributions (low confidence in target) lead to more conservative model updates, thereby attenuating the impact of overestimated values. This work deepens the understanding of distributional reinforcement learning, offering insights into the algorithmic mechanisms governing convergence and value estimation.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.08104 [cs.LG]
(or arXiv:2605.08104v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.08104
arXiv-issued DOI via DataCite
Submission history
From: Vanya Aziz [view email] [v1] Sun, 26 Apr 2026 19:37:54 UTC (1,714 KB)
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