Q Learning in Reinforcement Training Basics

Q Learning in Reinforcement Training Basics

位教师：Priyanka Mehta

访问权限由 New York State Department of Labor 提供

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

要了解的详细信息

可分享的证书

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作业

6 项作业

授课语言：英语（English）

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该课程共有2个模块

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.

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.

涵盖的内容

5个视频1篇阅读材料3个作业

5个视频总计12分钟

What is Q-Learning? 3分钟
Key Components of Q-Learning: Q value 1分钟
Key Components of Q-Learning: Rewards & Episodes 1分钟
Key Components of Q-Learning: Temporal Difference 4分钟
Key Components of Q-Learning: Exploration & Exploitation 3分钟

1篇阅读材料总计10分钟

Course Syllabus 10分钟

3个作业总计52分钟

Quiz on Introduction to Q-Learning 9分钟
Quiz on Key Components of Q-Learning 15分钟
Assessment for Fundamentals of Q-Learning 28分钟

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.