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
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==0.17.3`
        - `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)
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
