FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensionality'' induces severe exploration inefficiency and training instability. Consequently, highly optimized deterministic policy gradients currently dominate high-throughput regimes. We address this limitation with FastDSAC, a framework that effectively unlocks the potential of maximum entropy stochastic policies for complex continuous control. We introduce Dimension-wise Entropy Modulation (DEM) to dynamically redistribute the exploration budget, alongside a continuous distributional critic tailored to ensure accurate value estimation by mitiga...
| Authors | Jun Xue, Junze Wang, Shanze Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 4 May 2026 |
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