Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

ICLR 2019 Felipe Petroski SuchVashisht MadhavanEdoardo ContiJoel LehmanKenneth O. StanleyJeff Clune

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems... (read more)

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