Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/nikhilbarhate99/PPO-PyTorch
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
https://github.com/nikhilbarhate99/PPO-PyTorch
deep-learning deep-reinforcement-learning policy-gradient ppo ppo-pytorch proximal-policy-optimization pytorch pytorch-implmention pytorch-tutorial reinforcement-learning reinforcement-learning-algorithms
Last synced: about 2 months ago
JSON representation
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
- Host: GitHub
- URL: https://github.com/nikhilbarhate99/PPO-PyTorch
- Owner: nikhilbarhate99
- License: mit
- Created: 2018-09-27T15:07:12.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-12-08T20:54:57.000Z (10 months ago)
- Last Synced: 2024-06-17T19:04:33.372Z (3 months ago)
- Topics: deep-learning, deep-reinforcement-learning, policy-gradient, ppo, ppo-pytorch, proximal-policy-optimization, pytorch, pytorch-implmention, pytorch-tutorial, reinforcement-learning, reinforcement-learning-algorithms
- Language: Python
- Homepage:
- Size: 12.1 MB
- Stars: 1,537
- Watchers: 9
- Forks: 331
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PPO-PyTorch
### UPDATE [April 2021] :
- merged discrete and continuous algorithms
- added linear decaying for the continuous action space `action_std`; to make training more stable for complex environments
- added different learning rates for actor and critic
- episodes, timesteps and rewards are now logged in `.csv` files
- utils to plot graphs from log files
- utils to test and make gifs from preTrained networks
- `PPO_colab.ipynb` combining all the files to train / test / plot graphs / make gifs on google colab in a convenient jupyter-notebook
#### [Open `PPO_colab.ipynb` in Google Colab](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb) [](https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb)
## Introduction
This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective for OpenAI gym environments. It is primarily intended for beginners in [Reinforcement Learning](https://en.wikipedia.org/wiki/Reinforcement_learning) for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code. A concise explaination of PPO algorithm can be found [here](https://stackoverflow.com/questions/46422845/what-is-the-way-to-understand-proximal-policy-optimization-algorithm-in-rl) and a thorough explaination of all the details for implementing best performing PPO can be found [here](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/) (All are not implemented in this repo yet).
To keep the training procedure simple :
- It has a **constant standard deviation** for the output action distribution (**multivariate normal with diagonal covariance matrix**) for the continuous environments, i.e. it is a hyperparameter and NOT a trainable parameter. However, it is **linearly decayed**. (action_std significantly affects performance)
- It uses simple **monte-carlo estimate** for calculating advantages and NOT Generalized Advantage Estimate (check out the OpenAI spinning up implementation for that).
- It is a **single threaded implementation**, i.e. only one worker collects experience. [One of the older forks](https://github.com/rhklite/Parallel-PPO-PyTorch) of this repository has been modified to have Parallel workers
## Usage
- To train a new network : run `train.py`
- To test a preTrained network : run `test.py`
- To plot graphs using log files : run `plot_graph.py`
- To save images for gif and make gif using a preTrained network : run `make_gif.py`
- All parameters and hyperparamters to control training / testing / graphs / gifs are in their respective `.py` file
- `PPO_colab.ipynb` combines all the files in a jupyter-notebook
- All the **hyperparameters used for training (preTrained) policies are listed** in the [`README.md` in PPO_preTrained directory](https://github.com/nikhilbarhate99/PPO-PyTorch/tree/master/PPO_preTrained)
#### Note :
- if the environment runs on CPU, use CPU as device for faster training. Box-2d and Roboschool run on CPU and training them on GPU device will be significantly slower because the data will be moved between CPU and GPU often
## Citing
Please use this bibtex if you want to cite this repository in your publications :
@misc{pytorch_minimal_ppo,
author = {Barhate, Nikhil},
title = {Minimal PyTorch Implementation of Proximal Policy Optimization},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/nikhilbarhate99/PPO-PyTorch}},
}
## Results
| PPO Continuous RoboschoolHalfCheetah-v1 | PPO Continuous RoboschoolHalfCheetah-v1 |
| :-------------------------:|:-------------------------: |
|  |  |
| PPO Continuous RoboschoolHopper-v1 | PPO Continuous RoboschoolHopper-v1 |
| :-------------------------:|:-------------------------: |
|  |  |
| PPO Continuous RoboschoolWalker2d-v1 | PPO Continuous RoboschoolWalker2d-v1 |
| :-------------------------:|:-------------------------: |
|  |  |
| PPO Continuous BipedalWalker-v2 | PPO Continuous BipedalWalker-v2 |
| :-------------------------:|:-------------------------: |
|  |  |
| PPO Discrete CartPole-v1 | PPO Discrete CartPole-v1 |
| :-------------------------:|:-------------------------: |
|  |  |
| PPO Discrete LunarLander-v2 | PPO Discrete LunarLander-v2 |
| :-------------------------:|:-------------------------: |
|  |  |
## Dependencies
Trained and Tested on:
```
Python 3
PyTorch
NumPy
gym
```
Training Environments
```
Box-2d
Roboschool
pybullet
```
Graphs and gifs
```
pandas
matplotlib
Pillow
```
## References
- [PPO paper](https://arxiv.org/abs/1707.06347)
- [OpenAI Spinning up](https://spinningup.openai.com/en/latest/)