Library for Fast Offline RL Analysis with Minimum Dependencies
Project description
lightRaven -- Offline RL with Maximum Speed
This library provides convenient tools for people to create their own seldonian algorithms with optimum performance. A detailed example is also included in dynamic_training.ipynb. Performance test is in ci_performance.ipynb.
Dependencies
gym==0.17.3numpy==1.19.1scipy==1.5.2numba == 0.51.2
Supplementary Materials
- Definition of Seldonian Framework
- Definition of different Importance Sampling estimators
- Definition of the new concentration bound
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
lightRaven-0.1.0.tar.gz
(16.0 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file lightRaven-0.1.0.tar.gz.
File metadata
- Download URL: lightRaven-0.1.0.tar.gz
- Upload date:
- Size: 16.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8d038863a7b445d9557b8acd9c05f4f31f13784d36643d103c961c3ab856464e
|
|
| MD5 |
35efd8fde01ff5897aeb004917e669b6
|
|
| BLAKE2b-256 |
9568589fc017f39de266bd88a7a036f2aa03d0959b5937441f2ca502100f577b
|
File details
Details for the file lightRaven-0.1.0-py3-none-any.whl.
File metadata
- Download URL: lightRaven-0.1.0-py3-none-any.whl
- Upload date:
- Size: 23.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d00fee161046e6065ec5c3b0312e929b59133007840f6c107c6740d8dce7db11
|
|
| MD5 |
2f5a5cea7bb50ec4d3ff601b613ae6cd
|
|
| BLAKE2b-256 |
06ecaa809461703f0306ef91e94535541338edc439c0bb42964d9363ad36be84
|
