  We present a novel algorithm -- convex natural evolutionary strategies
(CoNES) -- for optimizing high-dimensional blackbox functions by leveraging
tools from convex optimization and information geometry. CoNES is formulated as
an efficiently-solvable convex program that adapts the evolutionary strategies
(ES) gradient estimate to promote rapid convergence. The resulting algorithm is
invariant to the parameterization of the belief distribution. Our numerical
results demonstrate that CoNES vastly outperforms conventional blackbox
optimization methods on a suite of functions used for benchmarking blackbox
optimizers. Furthermore, CoNES demonstrates the ability to converge faster than
conventional blackbox methods on a selection of OpenAI's MuJoCo reinforcement
learning tasks for locomotion.
