wassname/pytorch-a2c-ppo-acktr
1
0
Fork 0
mirror of https://github.com/wassname/pytorch-a2c-ppo-acktr.git synced 2026-06-27 16:20:05 +08:00
Code Issues Packages Projects Releases Wiki Activity
9 Commits 1 Branch 0 Tags
f09b3a75e42c36c4ae8cc041c75c9cf7d8fbd2ae
T
Clone
Open with VS Code Open with VSCodium Open with Intellij IDEA
Download ZIP Download TAR.GZ Download BUNDLE
Ilya Kostrikov f09b3a75e4 Add GAE
2017-09-16 20:57:06 -04:00
imgs
Add images
2017-09-14 07:08:50 -04:00
envs.py
Initial commit
2017-09-07 19:45:57 -04:00
LICENSE
Create LICENSE
2017-09-07 20:01:02 -04:00
main.py
Add GAE
2017-09-16 20:57:06 -04:00
model.py
Initial commit
2017-09-07 19:45:57 -04:00
README.md
Add images
2017-09-14 07:08:50 -04:00
vizualize_atari.py
Add visualization
2017-09-13 18:34:13 -04:00

README.md

pytorch-a2c

This is a PyTorch implementation of Advantage Actor Critic (A2C), a synchronous deterministic version of A3C "Asynchronous Methods for Deep Reinforcement Learning". Also see the OpenAI post (section A2C and A3C) for more information.

This implementation is inspired by the OpenAI A2C baseline. It uses the same hyper parameters and the model since they were well tuned for Atari games.

Contributions

Contributions are very welcome. If you know how to make this code better, don't hesitate to send a pull request.

Usage

python main.py --env-name "PongNoFrameskip-v4"

Results

BreakoutNoFrameskip-v4

SeaquestNoFrameskip-v4

QbertNoFrameskip-v4

beamriderNoFrameskip-v4

More results coming soon.

Reference in New Issue View Git Blame Copy Permalink
S
Description
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR).
Readme MIT 233 KiB
Languages
Python 100%