Artificial Intelligence IV - Reinforcement Learning in Java faq

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learnersLearners: 1,842
instructor Instructor: Holczer Balazs instructor-icon
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This course is perfect for those interested in Artificial Intelligence and Reinforcement Learning. It covers the mathematical background of Reinforcement Learning, such as Markov Decision Processes, value-iteration, policy-iteration and Q-learning. It also covers pathfinding algorithms with Q-learning and Q-learning with neural networks. This course is a great way to learn the state-of-the-art approach to Reinforcement Learning and gain a better understanding of Artificial Intelligence.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Paid

providerProvider:

Udemy

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

2021-12-17

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 18th, 2023]

Skills and Knowledge:
This course on Reinforcement Learning in Java will provide students with the skills and knowledge to understand and apply Markov Decision Processes, value-iteration and policy-iteration, Q-learning fundamentals, pathfinding algorithms with Q-learning, and Q-learning with neural networks. Students will gain an understanding of the state-of-the-art approach of Q-learning and how to interact with the environment to learn the optimal policy.
Professional Growth:
This course on Reinforcement Learning in Java provides a comprehensive overview of the mathematical background and algorithms used in this field. It covers topics such as Markov Decision Processes, value-iteration and policy-iteration, Q-learning fundamentals, pathfinding algorithms with Q-learning, and Q-learning with neural networks. By taking this course, professionals can gain a better understanding of the concepts and algorithms used in Reinforcement Learning, allowing them to apply these techniques to their own projects and further their professional growth.
Further Education:
This course on Reinforcement Learning in Java is suitable for preparing further education. It covers the mathematical background of reinforcement learning, including Markov Decision Processes, value-iteration, policy-iteration, and Q-learning. It also covers pathfinding algorithms with Q-learning and Q-learning with neural networks. This course provides a comprehensive overview of the fundamentals of reinforcement learning, making it an ideal choice for those looking to further their education in this field.

Course Syllabus

Introduction

Markov Decision Process (MDP) Theory

Markov Decision Process - Value Iteration

Markov Decision Process - Policy Iteration

Q Learning Theory

Pathfinding with Q-Learning

Exploration vs. Exploitation Problem

Deep Reinforcement Learning Theory

Course Materials (DOWNLOADS)

Course Provider

Provider Udemy's Stats at AZClass

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faq FAQ for Reinforcement Learning Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a paid certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: How many people have enrolled in this course?

So far, a total of 1842 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

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