1.5k citations · 2.8k across the 17 of their papers we have counts for
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cs.NE2018★ 1 cited
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu +8
Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less…
cs.NE2017★ 544 cited
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti +3
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algo…