https://github.com/openai/epg
Code for the paper "Evolved Policy Gradients"
https://github.com/openai/epg
continuous-control evolutionary-strategy machine-learning meta-learning paper reinforcement-learning
Last synced: 4 months ago
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Code for the paper "Evolved Policy Gradients"
- Host: GitHub
- URL: https://github.com/openai/epg
- Owner: openai
- License: mit
- Created: 2018-02-23T00:54:49.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-11-22T06:05:34.000Z (over 6 years ago)
- Last Synced: 2025-03-03T22:55:35.280Z (4 months ago)
- Topics: continuous-control, evolutionary-strategy, machine-learning, meta-learning, paper, reinforcement-learning
- Language: Python
- Homepage: https://arxiv.org/abs/1802.04821
- Size: 457 KB
- Stars: 249
- Watchers: 15
- Forks: 56
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
**Status:** Archive (code is provided as-is, no updates expected)
# Evolved Policy Gradients (EPG)
The paper is located at https://arxiv.org/abs/1802.04821. A demonstration video can be found at https://youtu.be/-Z-ieH6w0LA.
> Houthooft, R., Chen, R. Y., Isola, P., Stadie, B. C., Wolski, F., Ho, J., Abbeel, P. (2018). Evolved Policy
Gradients. arXiv preprint arXiv:1802.04821.
### Installation
Install Anaconda:
```
curl -o /tmp/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
bash /tmp/miniconda.sh
conda create -n epg python=3.6.1
source activate epg
```
Install necessary OSX packages for MPI:
```
brew install open-mpi
```
Install necessary Python packages:
```
pip install mpi4py==3.0.0 scipy \
pandas tqdm joblib cloudpickle == 0.5.2 \
progressbar2 opencv-python flask >= 0.11.1 matplotlib pytest cython \
chainer pathos mujoco_py 'gym[all]'
```
### Running
First go to the EPG code folder:
```
cd
```
Then launch the entry script:
```
PYTHONPATH=. python epg/launch_local.py
```
Experiment data is saved in `/EPG_experiments/-/`.
### Testing
First, set `theta_load_path = '/theta.npy'` in `launch_local.py` according to the `theta.npy` obtained after running the `launch_local.py` script. This file should be located in `//EPG_experiments/-//thetas/`.
Then run:
```
PYTHONPATH=. python epg/launch_local.py --test true
```
### Visualizing experiment data
Assuming the experiment data is saved in `/EPG_experiments/-/`, run:
```
PYTHONPATH=. python epg/viskit/frontend.py /EPG_experiments/-/
```
Then go to `http://0.0.0.0:5000` in your browser.
Viskit sourced from
> Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P. "Benchmarking Deep Reinforcement Learning for Continuous Control". Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016.
### BibTeX entry
```
@article{Houthooft18Evolved,
author = {Houthooft, Rein and Chen, Richard Y. and Isola, Phillip and Stadie, Bradly C. and Wolski, Filip and Ho, Jonathan and Abbeel, Pieter},
title = {Evolved Policy Gradients},
journal={arXiv preprint arXiv:1802.04821},
year = {2018}}
```