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 […]