Fundamentals of Deep Reinforcement Learning
Offered By: Learn Ventures via edX
Course Description
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
Taught by
Xander Steenbrugge, Frank Washburn and Shalev NessAiver
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