Advanced Reinforcement Learning

  • Duration 5h
  • Total Enrolled 1
  • Last Update December 22, 2020


This course is all about the application of deep learning and neural networks to reinforcement learning. If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace. The state of California is changing its regulations so that self-driving car companies can test their cars without a human in the car to supervise. We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.


What Will I Learn?

  • Build various deep learning agents (including DQN and A3C)

Topics for this course


Reinforcement Learning Concepts?

define Reinforcement learning along with the important terms that are used to formulate Reinforcement learning problems

Comparing Reinforcement Learning with ML?

compare the difference between the implementations of Reinforcement learning and Machine using Supervised and Unsupervised learning

Reinforcement Learning Use Cases?

describe the capabilities of Reinforcement learning illustrating its uses cases and example implementations

Reinforcement Learning Terms and Workflow?

recognize the essential terms of Reinforcement learning that are used and plays important roles in building Reinforcement learning workflows

Reinforcement Learning Implementation Approaches?

recall the prominent approaches of implementing Reinforcement learning

About the instructor

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1 Courses

1 students


Material Includes

  • 10.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion


  • Know reinforcement learning basics, MDPs, Dynamic Programming, Monte Carlo, TD Learning
  • College-level math is helpful
  • Experience building machine learning models in Python and Numpy
  • Know how to build ANNs and CNNs using Theano or Tensorflow

Target Audience

  • Professionals and students with strong technical backgrounds who wish to learn state-of-the-art AI techniques