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Course Outline
Introduction
Reinforcement Learning Basics
Basic Reinforcement Learning Techniques
Introduction to BURLAP
Convergence of Value and Policy Iteration
Reward Shaping
Exploration
Generalization
Partially Observable MDPs
Options
Logistics
TD Lambda
Policy Gradients
Deep Q-Learning
Topics in Game Theory
Summary and Next Steps
Requirements
- Proficiency in Python
- An understanding of college Calculus and Linear Algebra
- Basic understanding of Probability and Statistics
- Experience creating machine learning models in Python and Numpy
Audience
- Developers
- Data Scientists
21 Hours