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Professional Course

Fundamentals of Deep Reinforcement Learning

edX, Online
Length
8 weeks
Price
75 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Length
8 weeks
Price
75 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Visit this course's homepage on the provider's site to learn more or book!

Course description

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").

Upcoming start dates

1 start date available

Start anytime

  • Self-paced Online
  • Online
  • English

Who should attend?

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

Training content

  • 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

Course delivery details

This course is offered through Learn Ventures, a partner institute of EdX.

2-6 hours per week

Costs

  • Verified Track -$75
  • Audit Track - Free

Certification / Credits

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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