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.

翻译：以衍生为基础的优化技术,如Stochastistic 梯子,在培养深层神经网络方面非常成功。 但是,它也有端到端网络差异等制约因素。 作为替代办法,我们介绍了加速神经进化算法。新的算法旨在根据实验机器人学习方法完成物理机器人学习任务。我们首先在模拟任务上测试我们的算法,即玩游戏飞禽游戏,然后在静态物体中心任务中用实际的NAO机器人。代理商在相对较少的几代人中成功地导航了给定任务。根据我们的结果,我们提议在更复杂的任务中使用算法。