Artificial Intelligence IV - Reinforcement Learning in Java (Udemy.com)

All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach

Created by: Holczer Balazs

Produced in 2021

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What you will learn

  • Understand reinforcement learning
  • Understand Markov Decision Processes
  • Understand value- and policy-iteration
  • Understand Q-learning approach and it's applications

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Quality Score

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Overall Score : 82 / 100

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Course Description

This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:
  • Markov Decision Processes
  • value-iteration and policy-iteration
  • Q-learning fundamentals
  • pathfinding algorithms with Q-learning
  • Q-learning with neural networks
Who this course is for:
  • Anyone who wants to understand artificial intelligence and reinforcement learning!

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Instructor Details

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Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.
Take a look at my website if you are interested in these topics!

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Reviews

4.1

7 total reviews

5 star 4 star 3 star 2 star 1 star
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By James Soh

Need more examples

By Kree Pup

Good

By Sudhindra Desai

Need to include more dataset and examples

By Michael Kehayes

No code implementation of Deep Q-Learning...just a hard coded example that doesn't include any deep learning libraries.

By Howard

Good information, but the course could be half the size, do to the amount of repeats.

By Jonathan P Castro

Very good!

By Manish Garg

Good Course. The explanations by the author of a complex topic is simple and easy to understand.

It is good introductory course on Q learning.