Awesome

AI Labs Neuroevolution Algorithms

This repo contains distributed implementations of the algorithms described in:

[1] Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

[2] 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 for NeuroEvolution (VINE)

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.

Accelerated Deep Neurevolution

The folder ./gpu_implementation contains an implementation that uses GPU more efficiently. Refer to README.md in that folder for further instructions.

How to run locally

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>

extra folder

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.

How to run in docker container

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