Hi! My name is Eric Yu, and I’ve created this repository to help beginners dive into the world of Proximal Policy Optimization (PPO) using PyTorch. This guide aims to provide you with a straightforward implementation of PPO that’s well-documented and structured. If you’re tired of navigating through overly complicated implementations with no clear understanding, then you’re in the right place!

Introduction

This tutorial is designed with the assumption that you have some knowledge of Python and Reinforcement Learning (RL). You should be familiar with the basic concepts of policy gradient (PG) algorithms and PPO itself. If you are new to these topics, I recommend starting with the following resources:

• OpenAI Introduction to RL
• An Intuitive Explanation of Policy Gradient
• PPO Stack Overflow Post

For this implementation, you should be aware that it assumes a continuous observation and action space, but you can modify it for discrete ones quite easily.

Usage

To get started, it’s advisable to create a Python virtual environment to keep your project organized:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Once your environment is set up, you can train your model from scratch using:

python main.py

If you want to test the model, you can use the following command:

python main.py --mode test --actor_model ppo_actor.pth

To train with existing actor-critic models, run:

python main.py --actor_model ppo_actor.pth --critic_model ppo_critic.pth

Note: To adjust hyperparameters or environments, modify them in main.py. I prefer this method instead of command-line arguments as it avoids overly long command entries.

How It Works

The main.py file is your entry point. It will handle parsing the arguments via arguments.py and initializes the environment and the PPO model. Depending on the specified mode (which defaults to training), it executes the respective functions.

Here’s an analogy to help you grasp how PPO works:

Think of the entire process as training a puppy (your model) to learn new tricks (optimized actions). You reward the puppy (through positive reinforcements) whenever it performs a trick correctly while gently guiding it to understand what to avoid (learning from mistakes) using the principles of PPO. Each training session is like taking the puppy to the park to practice, where different environments (puzzles or tasks) keep it engaged and learning efficiently.

All the learning magic is encapsulated in ppo.py. For an understanding of how everything functions, I recommend exploring my series on Medium. You can use this tutorial to get started with debugging using pdb or refer to the official documentation for detailed instructions.

Environments and Hyperparameters

You can explore different environments listed here. Remember, this PPO implementation works only with environments that have Box for both observation and action spaces.

Hyperparameters can be found here.

Results

For results and further insights, please refer to my Medium article.

Troubleshooting

If you encounter any issues while setting everything up or running the code, here are a few troubleshooting ideas:

• Ensure that all the required packages are installed by running the command to install dependencies again.
• Make sure you are using the correct Python version specified in the requirements.
• Check that the file paths to your model weights are correct.
• If your model runs out of memory during training, consider reducing the batch size.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.