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Columbus, United States · Study online with LSBA

Graduate Certificate in Reinforcement Learning (Advanced)

Advanced program teaching algorithms and techniques for reinforcement learning, preparing students for careers in AI, machine learning, and robotics
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2 months to complete
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Overview

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Learning outcomes

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Course content

1

Foundations Of Reinforcement Learning

2

Introduction To Deep Learning

3

Markov Decision Processes

4

Stochastic Processes

5

Dynamic Programming

6

Model-Based Reinforcement Learning

7

Model-Free Reinforcement Learning

8

Deep Reinforcement Learning

9

Policy Gradient Methods

10

Value-Based Methods

11

Actor-Critic Methods

12

Exploration-Exploitation Trade-Off

13

Multi-Agent Reinforcement Learning

14

Imitation Learning

15

Transfer Learning

16

Meta-Learning

17

Unsupervised Learning

18

Reinforcement Learning For Robotics

19

Reinforcement Learning For Game Playing

20

Advanced Topics In Reinforcement Learning

Career Path

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Key facts

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Why this course

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We offer immediate access to our course materials through our open enrollment system. This means:

  • The course starts as soon as you pay the course fee, instantly
  • No waiting periods or fixed start dates
  • Instant access to all course materials upon payment
  • Flexibility to begin at your convenience

This self-paced approach allows you to begin your professional development journey immediately, fitting your learning around your existing commitments.

We offer two flexible learning paths to suit your schedule:

  • Fast Track: Complete in 1 month with 3-4 hours of study per week
  • Standard Mode: Complete in 2 months with 2-3 hours of study per week

You can progress at your own pace and access the materials 24/7.

There are no formal entry requirements for this course. You just need:

  • A good command of English language
  • Access to a computer/laptop with internet
  • Basic computer skills
  • Dedication to complete the course
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
  • All assessments are online

Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United States
MC
Michael Carter
US · Course completed

I'm thoroughly impressed with the Graduate Certificate in Reinforcement Learning (Advanced) from Stanmore School of Business! As a professional in the field, I was looking to upskill and this course exceeded my expectations. The content was incredibly relevant and helped me achieve my learning goals, particularly in understanding the nuances of deep reinforcement learning. I appreciated the practical examples and case studies that illustrated key concepts, such as the use of Q-learning in robotics. The course materials were top-notch, and I found the video lectures to be engaging and easy to follow. Overall, I'm extremely satisfied with my learning experience and would highly recommend this course to anyone looking to advance their skills in reinforcement learning.

LH
Leila Hassan
EG · Course completed

I found the Graduate Certificate in Reinforcement Learning (Advanced) to be a great introduction to the field. As someone with a background in computer science, I was interested in exploring the applications of reinforcement learning in AI. The course content was useful in helping me understand the basics of reinforcement learning, including the different types of learning environments and the importance of exploration-exploitation trade-offs. I also appreciated the discussion forums, where I could interact with other students and get feedback on my assignments. One area for improvement could be the addition of more advanced topics, such as multi-agent reinforcement learning. Nevertheless, I'm happy with my learning experience and would recommend this course to others looking to get started with reinforcement learning.

CO
Catarina Oliveira
BR · Course completed

Wow, just wow! The Graduate Certificate in Reinforcement Learning (Advanced) from Stanmore School of Business was an amazing experience! I was a bit skeptical at first, but the course completely blew me away. The instructors were knowledgeable and enthusiastic, and the course materials were incredibly comprehensive. I loved the hands-on approach, where we got to implement our own reinforcement learning algorithms using popular libraries like TensorFlow. The course also covered some really interesting topics, such as reinforcement learning for game playing and robotics. I felt like I was learning something new and exciting every week. The community was also super supportive, and I made some great connections with other students. If you're interested in reinforcement learning, don't hesitate to sign up for this course - you won't regret it!

RK
Rahul Kapoor
IN · Course completed

I recently completed the Graduate Certificate in Reinforcement Learning (Advanced) from Stanmore School of Business, and I must say that it was a well-structured and informative course. The content was detailed and covered a wide range of topics, from the basics of reinforcement learning to more advanced subjects like deep reinforcement learning. I found the course materials to be of high quality, particularly the lecture notes and assignments. The instructors were also responsive to questions and provided helpful feedback. One thing that I found particularly useful was the emphasis on practical applications of reinforcement learning, such as autonomous vehicles and recommender systems. Overall, I'm satisfied with my learning experience and would recommend this course to others looking to gain a deeper understanding of reinforcement learning.





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Recently updated!

April 2026