https://github.com/yaricom/deep-ne-gpu

The Deep Neuroevolution GPU enhanced with integrated Atari ALE environment
https://github.com/yaricom/deep-ne-gpu

atari deep-learning deep-q-network deep-reinforcement-learning genetic-algorithm neuroevolution

Last synced: 11 days ago
JSON representation

The Deep Neuroevolution GPU enhanced with integrated Atari ALE environment

• Host: GitHub
• URL: https://github.com/yaricom/deep-ne-gpu
• Owner: yaricom
• License: other
• Created: 2019-10-17T12:50:42.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-04T11:17:17.000Z (about 5 years ago)
• Last Synced: 2024-11-05T15:51:51.568Z (about 2 months ago)
• Topics: atari, deep-learning, deep-q-network, deep-reinforcement-learning, genetic-algorithm, neuroevolution
• Language: Python
• Size: 96.7 KB
• Stars: 1
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# AI Labs - GPU Neuroevolution

This folder contains preliminary work done to implement GPU-based deep neuroevolution.

For problems like Atari where the policy evaluation takes a considerable amount of time it is advantageous to make use of GPUs to evaluate the Neural Networks. This code shows how it is possible to run Atari simulations in parallel using the GPU in a way where we can evaluate neural networks in batches and have both CPU and GPU operating at the same time.

This folder has code in prototype stage and still requires a lot of changes to optimize performance, maintanability, and testing. We welcome pull requests to this repo and have plans to improve it in the future. Although it can run on CPU-only, it is slower than our original implementation due to overhead. Once this implementation has matured we plan on distributing it as a package for easy installation. We included an implementation of the HardMaze, but the GA-NS implementation will be added later on.

## Installation

clone repo

```

git clone https://github.com/yaricom/deep-neuroevolution.git

```

create python3 virtual using Anaconda

```

conda create -n deep_ne python=3.5

conda activate deep_ne

```

install current version of tensorflow-gpu

```

conda install -c anaconda tensorflow-gpu

```

Follow instructions under ./gym_tensorflow/README on how to compile the optimized interfaces.

To train GA on Atari just run:

```

python ga.py -c configurations/ga_atari_config.json -o out

```

Random search (It's a special case of GA where 0 individuals become parents):

```

python ga.py -c configurations/rs_atari_config.json -o out

```

Evolution Strategies:

```

python es.py -c configurations/es_atari_config.json -o out

```

Visualizing policies is possible if you install gym with `pip install gym` and run:

```

python -m neuroevolution.display

```

We currently have one example policy but more will be added in the future.

# Credentials

Initial implementation created by the Uber AI lab and available at https://github.com/uber-research/deep-neuroevolution