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Jay Chen


Research Papers

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

J. Lehman, J. Chen, J. Clune, K. Stanley
While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. [...] [PDF]
The Genetic and Evolutionary Computation Conference (GECCO), 2018

ES Is More Than Just a Traditional Finite-Difference Approximator

J. Lehman, J. Chen, Jeff Clune, Kenneth O. Stanley
An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep reinforcement learning domains. It searches for neural network parameters by generating perturbations to the current set of parameters, checking their performance, and moving in the aggregate direction of higher reward. [...] [PDF]
The Genetic and Evolutionary Computation Conference (GECCO), 2018

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