Paper Repro: Deep Neuroevolution – Towards Data Science

In this post, we reproduce the recent Uber paper "Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning", which amazingly showed that simple genetic algorithms sometimes performed better than apparently advanced reinforcement learning algorithms on well studied problems such as Atari games. We will ourselves reach state of the art performance on Frostbite, a game that had stumped reinforcement learning algorithms for years before Uber finally solved it with this paper. We will also learn about the dark art of training neural networks using genetic algorithms. In a way this could be considered part 3 of my deep reinforcement learning, but I think this article can also stand alone. Note that unlike these previous tutorials, this post will be using PyTorch instead of Keras, mainly because this is what I personally have switched to, but also because PyTorch does happen to be more suited for this particular use case.