In this chapter, you will learn about the deep neuroevolution method, which can be used to train Deep Neural Networks (DNNs). DNNs are conventionally trained using backpropagation methods based on the descent of the error gradient, which is computed with respect to the weights of the connections between neural nodes. Although gradient-based learning is a powerful technique that conceived the current era of deep machine learning, it has its drawbacks, such as long training times and enormous computing power requirements.
In this chapter, we will demonstrate how deep neuroevolution methods can be used for reinforcement learning and how they considerably outperform traditional DQN, A3C gradient-based learning methods of training DNNs. By the end of this chapter, you will have a solid understanding of deep neuroevolution methods, and you'll also have practical...
