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Certificat En Apprentissage Par Renforcement (Advanced)

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Overview

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Learning outcomes

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Course content

1

Deep Reinforcement Learning

2

Advanced Q-Learning

3

Policy Gradient Methods

4

Actor-Critic Algorithms

5

Deep Deterministic Policy Gradients

6

Asynchronous Advantage Actor-Critic

7

Trust Region Policy Optimization

8

Proximal Policy Optimization

9

Model-Based Reinforcement Learning

10

Model-Free Reinforcement Learning

11

Off-Policy Reinforcement Learning

12

On-Policy Reinforcement Learning

13

Multi-Agent Reinforcement Learning

14

Imitation Learning

15

Inverse Reinforcement Learning

16

Reinforcement Learning With Function Approximation

17

Reinforcement Learning For Robotics

18

Reinforcement Learning For Game Playing

19

Reinforcement Learning For Continuous Control

20

Reinforcement Learning With Deep Neural Networks

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
Open enrolment · Start today

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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 thrilled to have completed the Certificat En Apprentissage Par Renforcement (Advanced) course at Stanmore School of Business! As a professional in the field, I was looking to enhance my skills in reinforcement learning, and this course exceeded my expectations. The course content was comprehensive, covering topics such as deep Q-learning and policy gradients. I particularly appreciated the practical examples and case studies, which helped me understand how to apply these concepts to real-world problems. The course materials were of high quality, and the instructors were knowledgeable and responsive. Overall, I'm extremely satisfied with my learning experience and would highly recommend this course to anyone looking to advance their knowledge in reinforcement learning.

AM
Arjun Mehta
IN · Course completed

Hey guys, I just finished the Certificat En Apprentissage Par Renforcement (Advanced) course and I'm so stoked! I was a bit skeptical at first, but the course really delivered. I loved the hands-on approach - we got to work on some really cool projects, like building a reinforcement learning model from scratch. The instructors were super helpful, and the community was really supportive. One thing that really stood out to me was the emphasis on experimentation and exploration. I learned so much from trying out different approaches and seeing what worked and what didn't. The course materials were pretty solid, but I did have to do some extra reading to fill in a few gaps. Overall, I'd definitely recommend this course to anyone looking to learn about reinforcement learning - just be prepared to put in the work!

RS
Rafael Silva
BR · Course completed

Eu estou absolutamente satisfeito com o curso Certificat En Apprentissage Par Renforcement (Advanced) da Stanmore School of Business! Como engenheiro de machine learning, eu estava procurando por um curso que me ajudasse a aprofundar meus conhecimentos em aprendizado por reforço, e este curso superou minhas expectativas. O conteúdo do curso foi extremamente relevante e atual, cobrindo tópicos como aprendizado por reforço profundo e aprendizado por reforço com múltiplos agentes. A qualidade dos materiais do curso foi excepcional, e os instrutores foram muito conhecidos e disponíveis. Um exemplo específico que me vem à mente é o projeto final, onde tivemos que desenvolver um modelo de aprendizado por reforço para resolver um problema real. Foi um desafio, mas ao final, eu me senti muito realizado e confiante em minhas habilidades. Eu altamente recomendo este curso a qualquer um que queira avançar seu conhecimento em aprendizado por reforço!

AH
Amira Hassan
EG · Course completed

I recently completed the Certificat En Apprentissage Par Renforcement (Advanced) course at Stanmore School of Business, and I must say it was a thoroughly enjoyable and enriching experience. As a data scientist, I was keen to expand my skillset in reinforcement learning, and this course provided me with a comprehensive understanding of the subject matter. The course materials were well-structured and easy to follow, with a good balance of theoretical and practical aspects. I particularly appreciated the detailed explanations of the mathematical concepts underlying reinforcement learning, as well as the numerous examples and case studies that illustrated the practical applications of these concepts. The instructors were knowledgeable and responsive, and the online community was active and supportive. One area for improvement could be the provision of more feedback on assignments and projects, but overall, I was very satisfied with the course and would recommend it to anyone looking to gain a deeper understanding of reinforcement learning.





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Recently updated!

May 2026