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LVx: Fundamentals of Deep Reinforcement Learning

Learn the theoretical foundations of Deep Learning through practical Python code.

Fundamentals of Deep Reinforcement Learning
8 weeks
2–6 hours per week
Self-paced
Progress at your own speed
This course is archived
Fundamentals of Deep Reinforcement Learning

About this course

Skip About this course

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").

At a glance

  • Institution: LVx
  • Subject: Computer Science
  • Level: Introductory
  • Prerequisites:

    • Requirements:

      • Proficiency with Python
      • Functions, classes, objects, loops
      • Basic familiarity with Jupyter notebooks

    Recommended Prerequisites:

    • Basic probability
      • Sampling from a normal distributon
      • Conditional probability notation
      • \mathbb{E}E - expectation
    • \SigmaΣ - the summation operator
  • Language: English
  • Video Transcript: English
  • Associated skills:Artificial Neural Networks, Deep Learning, Reinforcement Learning, Python (Programming Language), Q Learning

What you'll learn

Skip 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.

Syllabus

Skip 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

Who can take this course?

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Ways to take this course

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Certificate

Free

Price

$75 USD

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Unlimited

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Shareable certificate upon completion

Graded assignments and exams

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