# VINE_old1

**Repository Path**: mirrors/VINE_old1

## Basic Information

- **Project Name**: VINE_old1
- **Description**: VINE(Visual Inspector for Neuroevolution) 是用于神经演化的交互式数据可视化工具，由 Uber 公司开源
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: https://www.oschina.net/p/vine
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2018-03-27
- **Last Updated**: 2025-08-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## 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](https://arxiv.org/abs/1712.06567)

[2] [Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents](https://arxiv.org/abs/1712.06560)

Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found [here](https://github.com/openai/evolution-strategies-starter). 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](http://www.mujoco.org/). Please provide your own Mujoco license and binary

The article describing these papers can be found [here](https://eng.uber.com/deep-neuroevolution/)

## 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](https://eng.uber.com/vine/).

## 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](https://github.com/openai/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](https://docs.docker.com/install/linux/linux-postinstall/#manage-docker-as-a-non-root-user).

```
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
```