semvec/pypi-packages huggingface dataset:
lightRaven 0.1.0
Library for Fast Offline RL Analysis with Minimum Dependencies
License: MIT
Requires Python: >=3.7.0

# 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)