NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Last update: Dec 05, 2022

Overview

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

It is NEAT but the weights are trained with Natural Evolution Strategy and the weights are shared across genomes.

Requirements

numpy
cloudpickle

Install

pip install nesneat

Optional

matplotlib # To draw networks
mpi4py # For parallelization
gym # For examples

Usage

Check examples

PYTHONPATH="$(pwd):$PYTHONPATH" python examples/cartpole.py
# Or in parallel
PYTHONPATH="$(pwd):$PYTHONPATH" mpirun -np 2 python examples/cartpole.py

Owner

Göktuğ Karakaşlı

Computer Science MSc. and Electronics engineer working on Machine Learning, Robotics, Computer Vision.

GitHub Repository

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NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Related tags

Overview

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Requirements

Install

Optional

Usage

Owner

Göktuğ Karakaşlı

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AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

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[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

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