Fundamentals of Deep Reinforcement Learning

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").

• 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