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20172024
most citedDeep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

544 citations · 919 across the 7 of their papers we have counts for

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Showing 2017 · cs.AIShow all

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cs.AI2017★ 114 cited

Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such +3

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challeng…