AI RESEARCH
Soft Deterministic Policy Gradient with Gaussian Smoothing
arXiv CS.LG
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ArXi:2605.06228v1 Announce Type: new Deterministic policy gradient (DPG) is widely utilized for continuous control; however, it inherently relies on the differentiability of the critic with respect to the action during policy updates. This assumption is violated in practical control problems involving sparse or discrete rewards, leading to ill-defined policy gradients and unstable learning. To address these challenges, we propose a principled alternative based on a smoothed Bellman equation formulated via Gaussian smoothing.