explainable-rl-through-causality

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

强化学习中文教程（蘑菇书?），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

An elegant PyTorch deep reinforcement learning library.

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Fit interpretable models. Explain blackbox machine learning.

Official PyTorch implementation of PTS/PSRN: Fast and efficient symbolic expression discovery through parallelized tree search. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots