Fundamentals of Deep Reinforcement Learning

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Free Online Course: Fundamentals of Deep Reinforcement Learning provided by edX is a comprehensive online course, which lasts for 8 weeks long, 2-6 hours a week. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from edX. Fundamentals of Deep Reinforcement Learning is taught by Xander Steenbrugge, Frank Washburn and Shalev NessAiver.

Overview
  • This course starts from the very beginnings of Reinforcement Learning and works its way up to a complete understanding of Q-learning, one of the core reinforcement learning algorithms.

    In part II of this course, you'll use neural networks to implement Q-learning to produce powerful and effective learning agents (neural nets are the "Deep" in "Deep Reinforcement Learning").

Syllabus
    • Introduction to Reinforcment Learning
    • Bandit Problems
      • Epsilon Greedy Agent
    • Markov Decision Processes
      • Episode Returns
      • Returns and Discount Factors
    • The Bellman Equation
    • Iterative Policy Evaluation and Improvement
    • Policy Evaluation and Iteration
    • Dynamic Programming
    • Q-Learning and Sampling Based Methods
    • Monte Carlo Rollouts vs. Temporal Difference Learning
    • On-Policy Learning vs. Off-Policy Learning
    • Q-Learning
    • What's Next