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Reinforcement Learning: Go from Beginner to Expert

Master the essentials of Reinforcement Learning including multi-agent reinforcement learning through hands-on immersive learning.

Top-Rated 1,812+ Learners

Created By Enes Bilgin

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  • Expert-Taught Videos

  • Guided Hands-On Exercises

  • Outcome Focus

  • Auto-Graded Assessments

  • Cloud Labs

  • Recall Quizzes

  • Real-Time Insights

Starts at

100% Money-Back Guarantee

This Course is Not Available in your region

This Course Includes:

  • 9 Hours of On-Demand Videos
  • 30 Guided Hands-On Exercises
  • 8 Auto-Graded Assessments
  • 2 Assignment
  • 46 Recall Quizzes
  • 8 Ebooks

Training for Teams of 5+?

Transform your workforce with access to KnowledgeHut's top-rated courses.

    Starts at

    100% Money-Back Guarantee

    This Course is Not Available in your region

    This Course Includes:

    • 9 Hours of On-Demand Videos
    • 30 Guided Hands-On Exercises
    • 8 Auto-Graded Assessments
    • 2 Assignment
    • 46 Recall Quizzes
    • 8 Ebooks

    Training for Teams of 5+?

    Transform your workforce with access to KnowledgeHut's top-rated courses.

    What You Will Learn

    • Master the essentials of Multi-agent RL.
    • Explore three paradigms of Machine Learning.
    • Understand exploration and exploitation.
    • Explore Tabular Q and Deep Q
    • Learn to train multiple agents using RLib.
    • Explore Markov Chains and Decision process.
    Show More

    The KnowledgeHut Edge

    Superior Outcomes

    Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform.

    Immersive Learning

    Go beyond just videos and learn hands-on with guided exercises, assignments and more.

    World-Class Instructors

    Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM.

    Hands-On with Cloud Labs

    A fully-provisioned developer environment where you can practice your code right in your browser.

    Real-World Learning

    Get an intimate, insider look at leading companies in the field through real-world case studies.

    Industry-Vetted Curriculum

    Curriculum primed for industry relevance and developed with guidance from industry advisory boards.

    Curriculum

    Video preview 1.

    Learning Objective:

    Understand the paradigms of machine learning and the elements of a Reinforcement Learning problem. 

    Topics Play Video
    • Three Paradigms of Machine Learning
    • RL Success Stories
    • Elements of an RL Problem
    • Introduction to Gym
    • Training Your First RL Agent Using RLlib
    Video preview 2.

    Learning Objectives:

    Get introduced to contextual bandit problems and learn the advanced approaches to trade off exploration and exploitation. 

    Topics Play Video
    • Multi-Armed Bandit Setting
    • Exploration-Exploitation Trade-Off
    • Fundamental Approaches To Trade Off Exploration and Exploitation
    • Advanced Approaches To Trade Off Exploration and Exploitation
    • Introduction to Contextual Bandit Problems
    • A Practical Contextual Bandit Example
    • Deep Contextual Bandits
    • Exploration With Deep Contextual Bandits
    • A Practical Example With Deep Contextual Bandits
    Video preview 3.

    Learning Objective:

    Get a detailed understanding of Markov Chains, the Markov Reward Process, and the Markov decision process. 

    Topics Play Video
    • Introducing Markov Chains
    • Markov Reward Process
    • Markov Decision Process
    • Policy Evaluation and Iteration
    • Tabular Q-Learning
    • Practical Tabular Q-Learning Example
    • Deep Q-Learning
    • Using RLlib To Train a Deep Q Network
    • Policy-Based Methods
    • Using RLib To Train PPO Agent
    Video preview 4.

    Learning Objective:

    Learn what goes into implementing Reward Shaping and its disadvantages while also using memory to handle Partial Observability. 

    Topics Play Video
    • Introduction
    • Handling Sparse Rewards and Hard Exploration
    • Implement Reward Shaping
    • Disadvantages of Reward Shaping
    • Using Memory To Handle Partial Observability
    • Solving Stateless Cartpole Using LSTM
    • Overcoming Sim-to-Real Gap
    • Introduction to Multi-Agent Reinforcement Learning
    • Training Multiple Agents Using RLib
    • Multi-Agent Reinforcement Learning
    • Offline Reinforcement Learning
    • Conclusion and Other Advanced Topics

    Prerequisites

    • A basic understanding of linear algebra, calculus, and probability is essential.
    • Prior knowledge and skills in Python programming are required.

    What Our Learners Are Saying

    Enjoyed immersive learning with assignments focused on bettering my practical skills required to master RL

    A
    Anjali Agarwal

    Tester

    Expert-led videos and immersive learning makes this course unique and enjoyable.

    R
    Rahul Trivedi

    Data Specialist

    Understood key topics like RL success stories & the 3 paradigms of Machine  that helped me to upskill.

    S
    Steve Jacob

    ML Specialist

    Perfect for working professionals with a strict schedule looking to upskill. The RL course of KH has added a huge value to my CV.

    N
    Noah Griffins

    Data Architect

    The curriculum was expertly designed with super focus on important topics like Real-World Reinforcement Learning.

    K
    Kelly Noble

    ML Expert

    How Our Course Compares

    YouTube Videos Online Courses KnowledgeHut

    On-Demand Videos

    Immersive Learning Experience

    Hands-On with Cloud Labs

    Structured Curriculum

    Course Curated by Industry Experts

    Auto-Graded Assessments

    Lifetime Access to Courseware

    Course Advisor

    Enes Bilgin
    Enes Bilgin

    Principal AI Engineer

    Enes is an ML Engineer at Argo AI and Engineering Leadership Advisory Council Board Member at California State University, Chico. He was previously Principal AI Engineer and Tech Lead at Microsoft and Research Scientist at Amazon.

    Course Advisor

    Enes is an ML Engineer at Argo AI and Engineering Leadership Advisory Council Board Member at California State University, Chico. He was previously Principal AI Engineer and Tech Lead at Microsoft and Research Scientist at Amazon.

    Enes Bilgin
    Enes Bilgin

    Principal AI Engineer

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    Frequently Asked Questions

    Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:

    • LEARN with recall quizzes, interactive ebooks, and case studies
    • ASSESS your skills progression with diagnostic, module-level, and final assessments
    • PRACTICE with real-world simulations and Cloud Labs
    • GAIN INSIGHTS with real-time reports and analytics on how you're progressing, your learning challenges, and suggestions of sections to revisit to build competency in the required areas.

    Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.

    Yes! Upon passing this online course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

    KnowledgeHut’s online courses is well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.


    You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy. 

    Please make sure that your computer meets the following software and system requirements: 

    • Software Requirements: Internet browser
    • System Requirements: Windows or equivalent environment with Internet browser and high-speed Internet connectivity.

    Yes, it does! In the unlikely event that you are not satisfied with the course, and you wish to withdraw within the first seven days, we’ll issue a 100% refund. Refer to our Online Self-Paced Courses Refund Policy for more details.

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