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Reinforcement Learning

10 weeks
All levels
8 lessons
0 quizzes
0 students

Reinforcement Learning Certification and training course is designed for professionals to upgrade their knowledge of AI algorithms. As Reinforcement is a crucial part of machine learning that includes deep reinforcement, deep and machine learning. In simple words, this Artificial Intelligence Course is a great opportunity to get masters in the basic algorithms, dynamic programming, temporal difference learning under the supervision of certified instructors. Enrol quickly to learn and become a certified Reinforcement expert.

Course description

Objectives of the Reinforcement Learning certification

  • Deep neural networks of Q-learning
  • Learn reinforcement learning with RBF networks
  • Understand policy gradient methods with neural networks
  • Deep Q-learning with conventional neural networks
  • Create various deep learning agents
  • Advance reinforcement learning algorithms
  • Learn how RL fits in the broader umbrella of machine learning.


The requirements of Reinforcement Learning certification are:

  • Familiarity with reinforcement learning basics, MDPs, dynamic programming, and TD learning
  • Working experience in building machine learning models with python and Numpy
  • Know how to build a feed forward, convolutional, and recurrent neural network using Theano and sensor flow

What is the duration of the course?

The certification in reinforcement Learning will takeĀ around 18 hours to completion.

This certification will teach you:

  • Introduction and Logistics
  • Background Review
  • AI Gym and Basic Reinforcement Learning Techniques
  • TD Lambda
  • Policy Gradients
  • Deep Q-Learning
  • Theano and TensorFlow Basics Review
  • Appendix

What are the types of reinforcements?

There are four basic types of reinforcement as follows:

  • Positive
  • Negative
  • Punishment
  • Extinction

How deep learning is different from reinforcement learning?

Both are two aspects of Artificial Intelligence. Deep learning is a learning process which includes all the data set, then implements it. On the other hand reinforcement learning is dynamic learning to maximize the rewards.

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