Welcome to CleanRL, your go-to library for implementing Deep Reinforcement Learning (RL) algorithms! This article will guide you through getting started with CleanRL, troubleshoot common issues, and enlighten you with helpful analogies along the way. Let’s dive in!

What is CleanRL?

CleanRL is a robust library that provides single-file implementations of various RL algorithms. Think of it as a neatly organized toolbox where every tool (or algorithm) is clearly labeled and easily accessible. This makes it a great reference for those looking to understand the intricacies of different RL methods.

Getting Started with CleanRL

Let’s set the stage for your first experiment with CleanRL!

Prerequisites

• Python versions: 3.7.1 or 3.11
• Poetry 1.2.1+

Installation Steps

To run experiments locally, follow these steps:

git clone https://github.com/vwxyzjn/cleanrl.git
cd cleanrl
poetry install
# Alternatively, you could use poetry shell and do
# python run cleanrl/ppo.py
poetry run python cleanrl/ppo.py --seed 1 --env-id CartPole-v0 --total-timesteps 50000
# Open another terminal and enter
cd cleanrl
tensorboard --logdir runs

Experiment Tracking with Weights & Biases

To use experiment tracking with WandB, run:

wandb login  # only required for the first time
poetry run python cleanrl/ppo.py --seed 1 --env-id CartPole-v0 --total-timesteps 50000 --track --wandb-project-name cleanrltest

Understanding the Code: An Analogy

Imagine CleanRL as a cooking recipe book. Each recipe (algorithm implementation) is placed in a simple, single-page format. For instance, in the recipe for “PPO with Atari” (contained in ppo_atari.py)—just like following a clear recipe—each step is articulated plainly. You amalgamate ingredients (code components), bringing them together until the dish (your RL model) is served and ready to be tasted (evaluated).

Troubleshooting

If you encounter issues during setup or execution, here are a few troubleshooting tips:

• Ensure that you’re using a compatible version of Python.
• Double-check your internet connection for successful installation of dependencies.
• If you experience issues with TensorBoard, confirm that your log directory is correct.

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Conclusion

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.