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

Simple PyTorch hierarchical models.

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Clockwork Convnets for Video Semantic Segmentation

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Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification

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