|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Deep Neuroevolution||1,502||2 years ago||17||other||Python|
|Sparse Evolutionary Artificial Neural Networks||187||2 years ago||mit||Python|
|Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).|
|Awesome Deep Neuroevolution||111||2 years ago||1||apache-2.0|
|A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)|
|Tensorflow Neuroevolution||109||3 months ago||5||September 03, 2020||21||apache-2.0||Python|
|Neuroevolution Framework for Tensorflow 2.x focusing on modularity and high-performance. Preimplements NEAT, DeepNEAT, CoDeepNEAT, etc.|
|Neuroevolution||67||2 years ago||2||mit||Python|
|Neuroevolution as a direct policy search deep reinforcement learning method, implemented using Keras and DEAP.|
|Denser Models||47||5 years ago||1||lgpl-3.0||Python|
|Neft Godot||39||2 years ago||n,ull||mit||GDScript|
|Neuroevolution of Fixed Topology for Godot|
|Es_pytorch||20||a year ago||7||Python|
|High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters|
|Galapagos_nao||20||5 years ago||12||mit||Elixir|
|A playground for continual, interactive neuroevolution|
This repo contains distributed implementations of the algorithms described in:
 Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
 Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found here. The repo has been modified to run both ES and our algorithms, including our Deep Genetic Algorithm (DeepGA) locally and on AWS.
Note: The Humanoid experiment depends on Mujoco. Please provide your own Mujoco license and binary
The article describing these papers can be found here
./visual_inspector contains implementations of VINE, i.e., Visual Inspector for NeuroEvolution, an interactive data visualization tool for neuroevolution. Refer to
README.md in that folder for further instructions on running and customizing your visualization. An article describing this visualization tool can be found here.
./gpu_implementation contains an implementation that uses GPU more efficiently. Refer to
README.md in that folder for further instructions.
git clone https://github.com/uber-common/deep-neuroevolution.git
create python3 virtual env
python3 -m venv env . env/bin/activate
pip install -r requirements.txt
If you plan to use the mujoco env, make sure to follow mujoco-py's readme about how to install mujoco correctly
launch sample ES experiment
. scripts/local_run_exp.sh es configurations/frostbite_es.json # For the Atari game Frostbite . scripts/local_run_exp.sh es configurations/humanoid.json # For the MuJoCo Humanoid-v1 environment
launch sample NS-ES experiment
. scripts/local_run_exp.sh ns-es configurations/frostbite_nses.json . scripts/local_run_exp.sh ns-es configurations/humanoid_nses.json
launch sample NSR-ES experiment
. scripts/local_run_exp.sh nsr-es configurations/frostbite_nsres.json . scripts/local_run_exp.sh nsr-es configurations/humanoid_nsres.json
launch sample GA experiment
. scripts/local_run_exp.sh ga configurations/frostbite_ga.json # For the Atari game Frostbite
launch sample Random Search experiment
. scripts/local_run_exp.sh rs configurations/frostbite_ga.json # For the Atari game Frostbite
visualize results by running a policy file
python -m scripts.viz 'FrostbiteNoFrameskip-v4' <YOUR_H5_FILE> python -m scripts.viz 'Humanoid-v1' <YOUR_H5_FILE>
The extra folder holds the XML specification file for the Humanoid Locomotion with Deceptive Trap domain used in https://arxiv.org/abs/1712.06560. Use this XML file in gym to recreate the environment.
You can also run the code inside a docker container using docker and docker-compose.
See https://docs.docker.com/get-started/ for an introduction to docker.
See also https://docs.docker.com/compose/overview/ for an introduction to docker-compose.
Clone repo and enter the directory.
git clone https://github.com/uber-common/deep-neuroevolution.git cd deep-neuroevolution
Start the container launching the redis instance, use sudo if required, see also this page.
sudo docker-compose up
Open up a second terminal session into the container.
sudo docker exec -it deepneuro /bin/bash
Start the experiment of your choice as stated above. E.g.
cd ~/deep-neuroevolution/ . scripts/local_run_exp.sh es configurations/frostbite_es.json