A repository for summaries of recent explainable AI/Interpretable ML approaches

A game theoretic approach to explain the output of any machine learning model.

Code for the paper: "Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning"

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyp…

Gym for multi-agent reinforcement learning

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch