This repository is a comprehensive resource for Deep Reinforcement Learning (RL) enthusiasts looking to delve into the world of Atari gaming environments. Through a collection of Jupyter notebooks, I demonstrate the application of various state-of-the-art RL algorithms to tackle Atari games, providing both beginners and seasoned practitioners with valuable insights and practical examples.
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Diverse Algorithms: Explore the training processes of popular Deep RL algorithms, including Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Networks (DQN), Advantage Actor-Critic (A2C), and more.
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Gymnasium Environments: Utilize OpenAI's Gymnasium environments to simulate Atari games, offering a standardized platform for RL experimentation. Gymnasium Documentation
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Stable Baselines 3: Implement RL algorithms with ease using Stable Baselines 3, a powerful library built on top of PyTorch. Stable Baseline3 Documentation
- Clone the repository:
git clone https://github.com/YourUsername/Atari_Games-Deep_Reinforcement_Learning.git
- Install the required dependencies:
pip install -r requirements.txt
Navigate to the notebooks directory and open any desired Jupyter notebook to explore the training procedures of different RL algorithms on Atari games.
Contributions are welcome! Feel free to open issues or pull requests to suggest improvements, report bugs, or add new features.
This project is licensed under the MIT License.
A special thanks to Thomas Simonini for the inspiration and valuable insights that contributed to the development of this repository.
This repository was inspired by and learned from Deep Reinforcement Learning Class.