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").
What you'll learn
- 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.
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Length | 8 weeks |
Effort | 8 weeks, 2–6 hours per week |
Starts | On Demand (Start anytime) |
Cost | $75 |
From | Learn Ventures via edX |
Instructors | Xander Steenbrugge, Frank Washburn, Shalev NessAiver |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming |
Tags | Computer Science |
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Rating | Not enough ratings |
---|---|
Length | 8 weeks |
Effort | 8 weeks, 2–6 hours per week |
Starts | On Demand (Start anytime) |
Cost | $75 |
From | Learn Ventures via edX |
Instructors | Xander Steenbrugge, Frank Washburn, Shalev NessAiver |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming |
Tags | Computer Science |
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