  Black-box policy optimization is a class of reinforcement learning algorithms
that explores and updates the policies at the parameter level. This class of
algorithms is widely applied in robotics with movement primitives or
non-differentiable policies. Furthermore, these approaches are particularly
relevant where exploration at the action level could cause actuator damage or
other safety issues. However, Black-box optimization does not scale well with
the increasing dimensionality of the policy, leading to high demand for
samples, which are expensive to obtain in real-world systems. In many practical
applications, policy parameters do not contribute equally to the return.
Identifying the most relevant parameters allows to narrow down the exploration
and speed up the learning. Furthermore, updating only the effective parameters
requires fewer samples, improving the scalability of the method. We present a
novel method to prioritize the exploration of effective parameters and cope
with full covariance matrix updates. Our algorithm learns faster than recent
approaches and requires fewer samples to achieve state-of-the-art results. To
select the effective parameters, we consider both the Pearson correlation
coefficient and the Mutual Information. We showcase the capabilities of our
approach on the Relative Entropy Policy Search algorithm in several simulated
environments, including robotics simulations. Code is available at
https://git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps}{git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps.
