Distributional Reinforcement Learning via the Cramér Distance
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 effic...
| Authors | Vanya Aziz, Ivo Nowak, E. M. T Hendrix |
|---|---|
| Primary category | cs.LG |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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