Course Outline


Reinforcement Learning Basics

Basic Reinforcement Learning Techniques

Introduction to BURLAP

Convergence of Value and Policy Iteration

Reward Shaping



Partially Observable MDPs



TD Lambda

Policy Gradients

Deep Q-Learning

Topics in Game Theory

Summary and Next Steps


  • 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


  • Developers
  • Data Scientists
  21 Hours


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