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

The main neuroevolution paper from Uber. A simple genetic algorithm (GA) outperforms Q-learning (DQN) and policy gradients (A3C) on hard deep RL problems. The GA parallelizes better than (and is thus faster than) ES, A3C, and DQN. Surprisingly, on some games even random search substantially outperforms DQN, A3C, and ES (but not the GA).


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