Reinforcement Learning: From Foundations to Q-Learning

Course Description

Dive into the fascinating world of Reinforcement Learning with this comprehensive course that takes you from the very foundations to a complete understanding of Q-learning, a core algorithm in the field. This introductory-level course, offered by LVx, is designed to provide you with both theoretical knowledge and practical implementation skills in Python. The course is structured to build your understanding progressively, starting with the basics and culminating in the ability to create powerful learning agents using Q-learning techniques.

What Students Will Learn

• Fundamental concepts and theoretical underpinnings of Reinforcement Learning (RL)
• Practical implementation of RL concepts in Python to solve real-world problems
• The Bellman Equation, a core formula in RL
• Q-Learning algorithm and its advanced improvements
• Preparation for implementing RL algorithms using Deep Neural Networks (covered in Part II)
• Hands-on experience through detailed Jupyter notebooks for each concept

Pre-requisites

• Proficiency in Python programming
• Familiarity with functions, classes, objects, and loops in Python
• Basic knowledge of Jupyter notebooks
• Recommended: Basic understanding of probability concepts (e.g., sampling from normal distribution, conditional probability notation)
• Familiarity with mathematical notations such as expectation (E) and summation (Σ)

Course Coverage

• Introduction to Reinforcement Learning
• Bandit Problems and Epsilon Greedy Agent
• Markov Decision Processes
• The Bellman Equation
• Iterative Policy Evaluation and Improvement
• Dynamic Programming
• Q-Learning and Sampling Based Methods
• Monte Carlo Rollouts vs. Temporal Difference Learning
• On-Policy Learning vs. Off-Policy Learning
• Advanced Q-Learning techniques

Who This Course Is For

This course is ideal for computer science students, aspiring AI researchers, and software developers who want to expand their skillset in the rapidly growing field of Reinforcement Learning. It's perfect for those with a basic understanding of Python who are looking to delve into the exciting world of intelligent decision-making algorithms.

Real-World Applications

The skills acquired in this course have wide-ranging applications in various industries. Learners can apply RL techniques to:

• Develop autonomous systems in robotics
• Optimize resource allocation in business processes
• Create intelligent game-playing agents
• Improve recommendation systems in e-commerce
• Enhance energy management in smart grids
• Optimize traffic flow in transportation systems
• Develop adaptive learning systems in education technology

Syllabus

1. Introduction to Reinforcement Learning
2. Bandit Problems
   • Epsilon Greedy Agent
3. Markov Decision Processes
   • Episode Returns
   • Returns and Discount Factors
4. The Bellman Equation
5. Iterative Policy Evaluation and Improvement
6. Policy Evaluation and Iteration
7. Dynamic Programming
8. Q-Learning and Sampling Based Methods
9. Monte Carlo Rollouts vs. Temporal Difference Learning
10. On-Policy Learning vs. Off-Policy Learning
11. Q-Learning
12. What's Next

Conclusion

This course offers a perfect blend of theory and practice, equipping you with the knowledge and skills to tackle complex decision-making problems using Reinforcement Learning techniques. By the end of this course, you'll be well-prepared to dive into more advanced topics in RL and apply your skills to real-world challenges.