Q-Learning is a reinforcement learning algorithm that helps agents learn optimal actions by maximizing future rewards.

Who is this course for?

This course is designed for beginners, developers, and professionals seeking practical skills in reinforcement learning.

What will I learn in this course?

You’ll learn Q-Learning fundamentals, including Q-values, rewards, exploration vs. exploitation, and hands-on implementation.

What is Q-Learning, and why is it important?

Q-Learning is key to building intelligent systems that learn from experience to make better decisions over time.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Q Learning in Reinforcement Training Basics

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Q Learning in Reinforcement Training Basics

Instructeur : Priyanka Mehta

Inclus avec

2 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

2 heures à compléter

Planning flexible

Apprenez à votre propre rythme

2 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

2 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Grasp Q-Learning fundamentals and reinforcement learning concepts
Understand Q-values, rewards, episodes, and temporal difference
Balance exploration vs. exploitation in training AI agents
Implement Q-Learning models with hands-on demos for real-world use

Compétences que vous acquerrez

Catégorie : Agentic systems
Catégorie : Reinforcement Learning
Catégorie : Model Training

Détails à connaître

Certificat partageable

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Évaluations

6 devoirs

Enseigné en Anglais

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Il y a 2 modules dans ce cours

This foundational course on Q-Learning equips you with the essential knowledge to understand reinforcement learning concepts and apply them in real-world AI scenarios. Learn the fundamentals of Q-Learning, including Q-values, rewards, episodes, temporal difference, and the exploration vs. exploitation trade-off. Progress to applying Q-Learning by determining Q-values and guiding agent decision-making. Gain practical skills through step-by-step guided demos, where you’ll implement Q-Learning and see how agents optimize their actions in environments like robotics, gaming, and intelligent systems. Build the confidence to design adaptive AI models that learn and improve over time.

By the end of this course, you will be able to: Understand Q-Learning: Explain its role in reinforcement learning and decision-making Explore Key Components: Q-values, rewards, episodes, and temporal difference Apply Strategies: Balance exploration vs. exploitation for optimal agent behavior Implement Algorithms: Build and test Q-Learning models with guided demos Design Intelligent Systems: Apply Q-Learning in robotics, gaming, and AI projects Ideal for developers, analysts, and professionals seeking practical reinforcement learning skills.

Détails du module

Learn the fundamentals of Q-Learning, a key reinforcement learning algorithm for training intelligent agents. Start with an introduction to Q-Learning and understand its role in decision-making. Explore core components including Q-values, rewards, episodes, temporal difference, and the balance of exploration vs. exploitation. Build practical skills to implement Q-Learning and optimize agent performance in real-world applications.

Inclus

5 vidéos1 lecture3 devoirs

5 vidéosTotal 12 minutes

What is Q-Learning?3 minutes
Key Components of Q-Learning: Q value1 minute
Key Components of Q-Learning: Rewards & Episodes1 minute
Key Components of Q-Learning: Temporal Difference4 minutes
Key Components of Q-Learning: Exploration & Exploitation3 minutes

1 lectureTotal 10 minutes

Course Syllabus10 minutes

3 devoirsTotal 52 minutes

Quiz on Introduction to Q-Learning9 minutes
Quiz on Key Components of Q-Learning15 minutes
Assessment for Fundamentals of Q-Learning28 minutes

Learn to apply Q-Learning by understanding how Q-values are determined and used for agent decision-making. Explore the process of evaluating Q-values to guide optimal actions in reinforcement learning. Gain hands-on experience through guided demos, where you’ll implement Q-Learning step by step and build practical skills to train and optimize intelligent agents in real-world scenarios.