AI Projects: Reinforcement Learning

Welcome to my repository of personal AI projects! Here, I share the work I'm doing in the field of Reinforcement Learning (RL).

Contents

Reinforcement Learning Projects

• These projects explore different algorithms across various environments.
• I experiment with methods ranging from the cross-entropy method to model-based algorithms.
• The projects include implementations of:
  • Deep Q-Networks (DQN)
  • Actor-Critic Methods
  • State-of-the-Art Algorithms
  • And many more

Tasks to Do

• Add an index to organize different projects and algorithms.

How to Run the Code

On Local Machine

If you have a CUDA-powered machine and want to run the projects locally, follow these steps:

Windows

conda env create -f environment.yml -n ai_projects_env
conda activate ai_projects_env

Notice

• Make sure you have Conda installed.
• The projects are compatible with Python 3.11.

References

Here are some of the resources that have helped me on my journey:

• Deep Reinforcement Learning Hands-On

  • Description: This book offers great hands-on RL projects with a good mix of mathematical theory and applied RL. Highly recommended.

• Machine Learning with PyTorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python

  • Description: This book is a comprehensive guide to machine learning and deep learning with PyTorch, acting as both a step-by-step tutorial and a valuable reference.

Final Word

I hope this repository helps you with your personal projects. Feel free to contact me on my social accounts to discuss AI and share ideas!