In March 2017, OpenAI released a blog post on evolution strategies, an optimisation technique that has been around for several decades. The novelty of their paper was that they managed to apply the technique to deep neural networks in the context of reinforcement learning (RL) problems. Before this, the optimisation of deep learning RL models (with typically millions of parameters) was typically achieved with backpropagation. Using evolution strategies for deep neural network (DNN) optimisation seemingly unlocked an exciting new toolbox for deep learning researchers to play with. This week, Uber AI Research released a set of five papers which are all focussed on'neuroevolution'.