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

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 for understanding the PPO algorithm. It can still be used for complex environments but may require some hyperparameter-tuning or changes in the code.

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 of this repository has been modified to have Parallel workers

A concise explaination of PPO algorithm can be found here

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

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
• OpenAI Spinning up