KO
|
EN
gitlite โ search
Search
#typescript
#ai-agents
#deepseek-harness
#dsh-plugin
#open-source
#ai
#claude-code
#cli
#windows
#codex
#developer-tools
#dsh
EPG
โ 254
Open GitHub โ
Code for the paper "Evolved Policy Gradients"
Download README (.md)
Explore Similar Repositories
paperboy
:
A web frontend for scheduling Jupyter notebook reports
DddAndEFCore
:
Source code for the DDD and EF Core Pluralsight course
DesignPatterns
:
Simple repository containing one simple example for all existing patterns in C#
bugsnag-ruby
:
BugSnag error monitoring & reporting software for rails, sinatra, rack and ruby
ok_ip_proxy_pool
:
๐ฟ็ฌ่ซไปฃ็IPๆฑ (proxy pool) python๐ไธไธช่ฟok็IPไปฃ็ๆฑ
// repository documentation
Was this content helpful?
โ 0
(0 ratings)
Select Rating:
โ
โ
โ
โ
โ
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
EPG
?
Download (.md)
**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 <path_to_EPG_folder> ``` Then launch the entry script: ``` PYTHONPATH=. python epg/launch_local.py ``` Experiment data is saved in `<home_dir>/EPG_experiments/<month>-<day>/<experiment_name>`. ### Testing First, set `theta_load_path = '<path_to_theta.npy>/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 `/<home_dir>/EPG_experiments/<month>-<day>/<experiment_name>/thetas/`. Then run: ``` PYTHONPATH=. python epg/launch_local.py --test true ``` ### Visualizing experiment data Assuming the experiment data is saved in `<home_dir>/EPG_experiments/<month>-<day>/<experiment_name>`, run: ``` PYTHONPATH=. python epg/viskit/frontend.py <home_dir>/EPG_experiments/<month>-<day>/<experiment_name> ``` 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}} ```
