This repository contains all standard model-free and model-based(coming) RL algorithms in Pytorch. (May also contain some research ideas I am working on currently)
For C++ version of Pytorch-RL : Pytorch-RL-CPP
pytorch-rl implements some state-of-the art deep reinforcement learning algorithms in Pytorch, especially those concerned with continuous action spaces. You can train your algorithm efficiently either on CPU or GPU. Furthermore, pytorch-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend pytorch-rl according to your own needs. TL:DR : pytorch-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms.
Install Pytorch-rl from Pypi (recommended):
pip install pytorch-policy
Multiple GAN training tricks have been used because of the instability in training the generators and discriminators. Please refer to https://awesomeopensource.com/project/soumith/ganhacks for more information.
Even after using the tricks, it was really hard to train a GAN to convergence. However, after using Spectral Normalization (https://arxiv.org/abs/1802.05957) the infogan was trained to convergence.
For image to image translation tasks with GANs and for VAEs in general, training with Skip Connection really helps the training.