Learning the Supports for Categorical Critic in Reinforcement Learning
Value functions are an essential component in actor-critic based deep reinforcement learning (RL). Conventionally, these functions are trained as a regression task by minimising the mean squared error (MSE) relative to bootstrapped target values. Meanwhile, in distributional RL, a distribution of returns is modelled based on the distributional Bellman operator. This work investigates the Gaussian Histogram Loss (HL-Gauss), a recent approach that reframes value estimation as classification by encoding each scalar Bellman target as a Gaussian-smoothed categorical target. Despite its potential, applying histogram-based losses to RL presents inherent challenges, most notably the requirement t...
| Authors | Jen-Yen Chang, Takayuki Osa, Tatsuya Harada |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 7 July 2026 |
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