Reinforcement Learning Tutorials

Based on research, here are some of the most highly recommended tutorials for learning reinforcement learning:

Comprehensive Tutorial Collections

Neptune.ai’s Reinforcement Learning Tutorials

• RL with Mario Bros: Learn reinforcement learning through a unique tutorial based on Super Mario.
• Machine Learning for Humans: Reinforcement Learning: Part of an ebook that explains core concepts with numerous examples and easy-to-follow explanations.
• An Introduction to Reinforcement Learning: Comprehensive overview of reinforcement learning with processes, tasks, approaches, and introduction to deep reinforcement learning.
• Reinforcement Learning from Scratch: Tutorial by an author with experience at Unity Technologies, providing an overview of core concepts for beginners.
• Deep Reinforcement Learning for Automated Stock Trading: Solution for stock trading strategy using reinforcement learning, explaining PPO, A2C, and DDPG algorithms.
• Applications of Reinforcement Learning in Real World: Detailed study of RL applications in real-world projects across 8 areas of learning.
• Practical RL: Open-source course on GitHub with comprehensive syllabus, chat rooms, gradings, and FAQs.
• Simple Reinforcement Learning with TensorFlow: Tutorial series exploring Q-learning algorithms with implementation guidance.

Domain-Specific Tutorials

Game Development

• Deep Learning Flappy Bird: GitHub repository teaching deep Q learning algorithms through Flappy Bird implementation.
• Kaggle’s Intro to Game AI and Reinforcement Learning: Interactive mini-course focusing on applying RL to game development.

Natural Language Processing

• NLP with Reinforcement Learning: Tutorial showing RL in combination with NLP to beat question and answer adventure games.

Financial Applications

• Trading with Reinforcement Learning: Demonstrations of deep reinforcement learning techniques for stock market analysis.

Robotics and Autonomous Systems

• CARLA: Open-source simulator for autonomous driving research with integrated Conditional Reinforcement Learning models.
• Robotics RL Tutorials: Video demonstrations of autonomous reinforcement learning agents for robotics.

Other Applications

• Traffic Light Control: Multiple research papers and project examples for RL in traffic control systems.
• Marketing and Advertising: Tutorials on making AI systems learn from real-time interactions for creating advertising content.
• Healthcare Applications: Resources on optimizing AI in healthcare using reinforcement learning for detailed treatment plans.
• Recommendation Systems: Practical implementations of reinforcement learning algorithms in recommendation systems.

Framework-Specific Tutorials

TensorFlow

• Simple Reinforcement Learning with TensorFlow: Series on implementing Q-learning algorithms with TensorFlow.
• TensorForce: Open-source deep reinforcement learning framework specialized in TensorFlow.

PyTorch

• PyTorch Reinforcement Learning Tutorials: Various implementations of RL algorithms using PyTorch.

OpenAI Gym/Gymnasium

• Reinforcement Learning with Gymnasium: Practical guide to getting started with Gymnasium for developing and comparing RL algorithms.
• OpenAI Gym Tutorials: Resources for using OpenAI’s environments for reinforcement learning.

Video Tutorials

• Reinforcement Learning in 3 Hours | Full Course using Python: Comprehensive YouTube course covering fundamentals.
• DeepMind x UCL – Introduction to Reinforcement Learning: Comprehensive YouTube series covering foundational principles to advanced techniques.
• FreeCodeCamp’s Reinforcement Learning Course: Project-oriented YouTube course covering essentials and implementation.

Additional Resources

• Many tutorials include code repositories on GitHub
• Interactive environments like OpenAI Gym/Gymnasium are commonly used for practical implementation
• For beginners, starting with “Machine Learning for Humans: Reinforcement Learning” or “Reinforcement Learning from Scratch” is recommended