PyTorch implementation of Advantage Actor Critic
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 Adversarial Imitation Learning GAIL
Also see the OpenAI posts: A2C/ACKTR and PPO for more information.
This implementation is inspired by the OpenAI baselines for A2C, ACKTR and PPO. It uses the same hyper parameters and the model since they