Deterministic Policy Gradient

Definition

Deterministic Policy Gradient (DPG) learns a deterministic policy $a=\mu (s)$ as an actor on continuous action spaces and an Action-Value Function $Q(s,a)$ as a critic.

DPG requires fewer samples to approximate the gradient than stochastic Policy Gradient because DPG updates the parameter only over the state space, according to the Deterministic Policy Gradient Theorem. $\Delta \theta ={\nabla }_{a}Q(s,a){|}_{a=\mu (s;\theta )}{\nabla }_{\theta }\mu (s;\theta )$