  In recent years, Evolutionary Strategies were actively explored in robotic
tasks for policy search as they provide a simpler alternative to reinforcement
learning algorithms. However, this class of algorithms is often claimed to be
extremely sample-inefficient. On the other hand, there is a growing interest in
Differentiable Robot Simulators (DRS) as they potentially can find successful
policies with only a handful of trajectories. But the resulting gradient is not
always useful for the first-order optimization. In this work, we demonstrate
how DRS gradient can be used in conjunction with Evolutionary Strategies.
Preliminary results suggest that this combination can reduce sample complexity
of Evolutionary Strategies by 3x-5x times in both simulation and the real
world.
