LVx: Fundamentals of Deep Reinforcement Learning

• The theoretical underpinnings of Reinforcement Learning ("RL").
• How to implement each piece of theory to solve real problems in Python.
• The core RL formula: The Bellman Equation
• The Q-Learning algorithm, as well as many powerful improvements.
• Enough to prepare you for implement Reinforcement Learning algorithms using Deep Neural Networks (Part II).

Each concept is presented with a video overview, and detailed Jupyter notebooks covering each aspect of theory and practice.

• 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