  Policy optimization on high-dimensional continuous control tasks exhibits its
difficulty caused by the large variance of the policy gradient estimators. We
present the action subspace dependent gradient (ASDG) estimator which
incorporates the Rao-Blackwell theorem (RB) and Control Variates (CV) into a
unified framework to reduce the variance. To invoke RB, our proposed algorithm
(POSA) learns the underlying factorization structure among the action space
based on the second-order advantage information. POSA captures the quadratic
information explicitly and efficiently by utilizing the wide & deep
architecture. Empirical studies show that our proposed approach demonstrates
the performance improvements on high-dimensional synthetic settings and OpenAI
Gym's MuJoCo continuous control tasks.
