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Fundamentals of Deep Reinforcement Learning

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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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