UdemyArtificial IntelligencePaid courseAll LevelsCertificate

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

4.5

196 reviews

Created by: Holczer Balazs

Last updated January 2025

What you will learn

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

How it compares

Against the other 205 rated Udemy courses in Artificial Intelligence on CourseDuck:

Rating4.5 out of 5higher than 58% of them
Reviews196#142 of 206 by number of reviews
Length5.2 hourslonger than 50% of them
Last updatedJanuary 2025more recently than 50% of them

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 90 / 100

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!

All Levels

Time: 5.2h

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Instructor Details

Holczer Balazs

4.5 Rating
196 Reviews

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!