This repo contains distributed implementations of the algorithms described in:
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
The folder ./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.
The folder ./gpu_implementation contains an implementation that uses GPU more efficiently. Refer to README.md in that folder for further instructions.
clone repo
git clone https://github.com/uber-common/deep-neuroevolution.git
create python3 virtual env
python3 -m venv env
. env/bin/activate
install requirements
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 redis
. scripts/local_run_redis.sh
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.