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.

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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

PFLD pytorch Implementation

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Official implementation for "Symbolic Learning to Optimize: Towards Interpretability and Scalability"

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A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

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Style transfer between images was performed using the VGG19 model

Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

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MMFlow is an open source optical flow toolbox based on PyTorch

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SPT_LSA_ViT - Implementation for Visual Transformer for Small-size Datasets

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