In Machine Learning research, only reading a research paper is sometimes not enough to absorb all the knowledge and information that were produced. Deeply understanding the implementation is important too. To extrapolate from Feynman's famous quote, the best way to understand something is to create it. That explains the existence of these implementations.
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Pytorch Code
Proximal Policy Optimization code
Twin Delayed Deep Deterministic Policy Gradients Port of original code to python 3
Masked Autoencoder for Density Estimation in jax code