Train Gym-derived environments in Python/C++ through Ray RLlib
Project description
PyRLprob is an open-source python library for training, evaluation, and postprocessing of Gym-based environments, written in Python, through Ray-RLlib reinforcement learning library.
Installation
Use the package manager pip to install the latest stable release of pyRLprob, with all its dependencies:
pip install pyrlprob
To test if the package is installed correctly, run the following tests:
from pyrlprob.tests import *
test_train_eval_py()
If the code exits without errors, a folder named results/ with the test results will be created in your current directory.
User Guide
Credits
pyRLprob has been created by Lorenzo Federici in 2021. For any problem, clarification or suggestion, you can contact the author at lorenzof@arizona.edu.
License
The package is under the MIT license.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file pyrlprob-2.2.20.tar.gz.
File metadata
- Download URL: pyrlprob-2.2.20.tar.gz
- Upload date:
- Size: 200.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.9.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
623c8d60f4dd44b83c0e4597077abfeb7c3fb2116f341971a591f464898048d3
|
|
| MD5 |
2519a6a00d5262d6491054a4729c3810
|
|
| BLAKE2b-256 |
52d7dc55472ed95d07692e75d1c4c62c00ed09dcb16e3762e8648dc6f3116318
|
