Mastering AI: Advanced Reinforcement Learning
Mastering AI: Advanced Reinforcement Learning, available at $19.99, has an average rating of 4.5, with 21 lectures, based on 11 reviews, and has 14 subscribers.
You will learn about Gain a comprehensive understanding of the core principles and motivations behind reinforcement learning. Acquire practical skills in setting up and managing the software environment necessary for reinforcement learning development. Master the implementation of fundamental and advanced reinforcement learning algorithms using Python. Learn to design and generate random Markov Decision Processes (MDPs) to test and refine algorithms. Develop the ability to model and address the uncertainty inherent in various real-world environments. Solve complex reinforcement learning problems efficiently using modern tools like TensorFlow and RLlib. Execute and manage projects within the OpenAI Gym toolkit, including the Frozenlake challenge. Achieve proficiency in training and deploying AI agents capable of making autonomous decisions in dynamic settings. This course is ideal for individuals who are Beginners in the field of data science and machine learning or Anyone who wants to learn RL It is particularly useful for Beginners in the field of data science and machine learning or Anyone who wants to learn RL.
Enroll now: Mastering AI: Advanced Reinforcement Learning
Summary
Title: Mastering AI: Advanced Reinforcement Learning
Price: $19.99
Average Rating: 4.5
Number of Lectures: 21
Number of Published Lectures: 21
Number of Curriculum Items: 21
Number of Published Curriculum Objects: 21
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Gain a comprehensive understanding of the core principles and motivations behind reinforcement learning.
- Acquire practical skills in setting up and managing the software environment necessary for reinforcement learning development.
- Master the implementation of fundamental and advanced reinforcement learning algorithms using Python.
- Learn to design and generate random Markov Decision Processes (MDPs) to test and refine algorithms.
- Develop the ability to model and address the uncertainty inherent in various real-world environments.
- Solve complex reinforcement learning problems efficiently using modern tools like TensorFlow and RLlib.
- Execute and manage projects within the OpenAI Gym toolkit, including the Frozenlake challenge.
- Achieve proficiency in training and deploying AI agents capable of making autonomous decisions in dynamic settings.
Who Should Attend
- Beginners in the field of data science and machine learning
- Anyone who wants to learn RL
Target Audiences
- Beginners in the field of data science and machine learning
- Anyone who wants to learn RL
Stay Ahead with Continuous Updates: This course is committed to staying at the cutting edge of technology. We regularly infuse the curriculum with the latest advancements and updates in reinforcement learning technology, ensuring that what you learn not only meets but exceeds the current industry standards. Join us to keep your skills sharp and your knowledge fresh in the rapidly evolving field of artificial intelligence.
Welcome to “Mastering AI: Advanced Reinforcement Learning,” a course designed to transform you into a skilled practitioner capable of tackling real-world challenges using reinforcement learning (RL). RL is a sophisticated branch of artificial intelligence (AI) focused on developing agents that learn to make decisions autonomously, optimizing performance through trial and error.
Course Overview:
Starting with a comprehensive introduction to the fundamentals of RL, this course will guide you through a variety of environments and frameworks that are crucial for RL applications. You will learn how to create custom environments and leverage OpenAI baselines for implementing cutting-edge RL algorithms. By diving into classical RL techniques like Dynamic Programming, Monte Carlo methods, and Temporal-Difference (TD) Learning, you’ll gain a deep understanding of how to frame problems and refine solutions dynamically.
Advanced Modules:
As the course progresses, we’ll delve into more complex strategies, including deep Q-learning and other advanced RL algorithms. You’ll gain hands-on experience with state-of-the-art Python libraries such as TensorFlow and Ray’s RLlib, which are instrumental in building and scaling RL solutions. The course also covers the generation of random Markov Decision Processes (MDPs) and the intricacies of solving diverse RL challenges. You’ll learn to model the uncertainty inherent in real-life scenarios and effectively apply your knowledge to areas like healthcare, robotics, and consumer behavior modeling.
Practical Projects:
To solidify your learning, you will engage in practical projects like the Frozenlake challenge using the OpenAI Gym toolkit. These projects are designed to replicate industry-level problems, offering you the opportunity to apply theoretical knowledge to tangible tasks.
What You Will Gain:
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A robust understanding of reinforcement learning basics and its motivations.
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Expertise in configuring and managing necessary software for RL application development.
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Proficiency in implementing and adapting RL algorithms to solve complex problems.
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Skills to model and navigate through the uncertainties of various environments.
By the end of this course, you will have developed the competence to train and deploy advanced RL agents, preparing you to contribute effectively to AI projects or embark on a career in this dynamic field. Join us to unlock the full potential of AI through the power of reinforcement learning!
Course Curriculum
Chapter 1: Welcome
Lecture 1: Introduction
Lecture 2: Welcome Message
Chapter 2: Getting started
Lecture 1: Introduction
Lecture 2: Learn About Anaconda Virtual Environments
Lecture 3: Learn About OpenAI Gym
Lecture 4: Reinforcement Learning Basics
Lecture 5: Understanding Terminology
Lecture 6: OpenAI Gym Basics
Lecture 7: Understanding Environments
Chapter 3: Learning Key Concepts
Lecture 1: Markov Decision Processes
Lecture 2: Solving Markov Decision Processes
Lecture 3: Determine a Reinforcement Learning Problem
Chapter 4: Simple Reinforcement Learning Problems
Lecture 1: Solving Taxi Environment
Lecture 2: Solving Frozen Lake Environment
Lecture 3: Reward Discounting
Lecture 4: Section Summary
Chapter 5: Deep Reinforcement Learning
Lecture 1: Introduction
Lecture 2: Solving Mountain Car Environment
Lecture 3: Solving Pong Atari Game With TensorFlow
Chapter 6: Course Summary
Lecture 1: Summary
Chapter 7: Course Material & Source Code
Lecture 1: Course Material & Source Code
Instructors
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Justin Lambert
Data Scientist
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 1 votes
- 5 stars: 9 votes
Frequently Asked Questions
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