EDSR-PyTorch PyTorch version of the paper ‘Enhanced Deep Residual Networks for Single Image Super-Resolution’ (CVPRW 2017) About PyTorch 1.1.0 There have been minor changes with the 1.1.0 update. Now we support PyTorch 1.1.0 by default, and please use the legacy branch if you prefer older version. This repository is an official PyTorch implementation of the paper “Enhanced Deep Residual Networks for Single Image Super-Resolution” from CVPRW 2017, 2nd NTIRE.You can find the original code and more information from here. If […]

e2c-pytorch Paper can be found here: https://arxiv.org/abs/1506.07365 You will need a patched version of OpenAI Gym in order to generate the dataset. See https://github.com/ethanluoyc/gym/tree/pendulum_internal For the planar task, we use code from. The source code of the repository has been modified for our needs and included under e2c/e2c_tf. What’s included ? E2C model, VAE and AE baselines. Allow configuration for different network architecture for the different setups (see Appendix of the paper). TODO Documentation, tests… (Soon to follow) GitHub https://github.com/ethanluoyc/e2c-pytorch […]

pytorch-a2c-ppo-acktr PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL). Please use hyper parameters from this readme. With other hyper parameters things might not work (it’s RL after all)! This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version of A3C Proximal Policy Optimization PPO Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation ACKTR Generative […]