  Evolution Strategies (ES) are a powerful class of blackbox optimization
techniques that recently became a competitive alternative to state-of-the-art
policy gradient (PG) algorithms for reinforcement learning (RL). We propose a
new method for improving accuracy of the ES algorithms, that as opposed to
recent approaches utilizing only Monte Carlo structure of the gradient
estimator, takes advantage of the underlying MDP structure to reduce the
variance. We observe that the gradient estimator of the ES objective can be
alternatively computed using reparametrization and PG estimators, which leads
to new control variate techniques for gradient estimation in ES optimization.
We provide theoretical insights and show through extensive experiments that
this RL-specific variance reduction approach outperforms general purpose
variance reduction methods.
