  Derivative-based optimization techniques such as Stochastic Gradient Descent
has been wildly successful in training deep neural networks. However, it has
constraints such as end-to-end network differentiability. As an alternative, we
present the Accelerated Neuroevolution algorithm. The new algorithm is aimed
towards physical robotic learning tasks following the Experiential Robot
Learning method. We test our algorithm first on a simulated task of playing the
game Flappy Bird, then on a physical NAO robot in a static Object Centering
task. The agents successfully navigate the given tasks, in a relatively low
number of generations. Based on our results, we propose to use the algorithm in
more complex tasks.
