Metadata-Version: 2.1
Name: lightRaven
Version: 0.1.0
Summary: Library for Fast Offline RL Analysis with Minimum Dependencies
Home-page: https://github.com/M0gician/lightRaven
Author: Tommy Yang (m0g1cian)
Author-email: tommyyang0524@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: numba
Requires-Dist: gym


# 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==0.17.3`
- `numpy==1.19.1`
- `scipy==1.5.2`
- `numba == 0.51.2`

## Supplementary Materials
- Definition of Seldonian Framework
  - [Preventing undesirable behavior of intelligent machines](https://science.sciencemag.org/content/366/6468/999)
  - [High Confidence Policy Improvement](https://people.cs.umass.edu/~pthomas/papers/Thomas2015.pdf)
- Definition of different Importance Sampling estimators
  - [High Confidence Off-Policy Evaluation](https://people.cs.umass.edu/~pthomas/papers/Thomas2015.pdf)
- Definition of the new concentration bound 
  - [A New Confidence Interval for the Mean of a Bounded Random Variable](https://arxiv.org/abs/1905.06208)

