Applied Reinforcement Learning for Business Decision Optimization
4 Weeks
·Cohort-based Course
Do you want to apply reinforcement learning to real-world decision-making and drive business value? This course is for you
Applied Reinforcement Learning for Business Decision Optimization
4 Weeks
·Cohort-based Course
Do you want to apply reinforcement learning to real-world decision-making and drive business value? This course is for you
Course overview
Master the Reinforcement Learning for Better Business Decisions
💡 Why I Built This Course
Five years ago, I went into reinforcement learning—and quickly got overwhelmed.
The theory was complex.
The resources were scattered.
I couldn’t see how any of it connected to real-world problems.
It took me years to piece it all together. Eventually, I discovered something powerful: RL isn’t just an academic concept—it’s a practical tool for making better decisions in business. Once I connected RL with sequential decision analytics, everything clicked.
I finally saw how to use it for real business challenges like pricing, inventory, and operations.
This course is the guide I wish I had back then.
This course is not about games or robotics. It’s about turning RL into business value.
Over four weeks, you’ll learn to frame business problems as RL problems—and solve them step by step using Python. You won’t be doing it alone. You’ll be learning alongside peers, with hands-on support every week.
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⭐️ Course Benefits & Outcomes
1) Confused by Reinforcement Learning theory → Confident in practical application
Follow a clear, structured learning path that takes the mystery out of RL and shows you exactly how to apply it to real-world business decisions.
2) Scattered blog posts & research papers → Business-ready RL skills
Solve real-world problems through Python programming - no more bouncing between tutorials that never connect to business reality.
3) Games & robotics → Real business use cases
We’re not teaching RL for video games or self-driving cars. This course is focused on business-relevant problems like pricing, inventory, and customer journeys.
4) “I know RL exists” → “I use RL to optimize decisions”
Learn how RL fits into the broader landscape of **sequential decision analytics**, and walk away ready to model, simulate, and improve decisions in your business domain.
5) "Do not know how RL play role in LLM" → " I know why RL is used in LLMS"
Gain confidence and understanding of the state-of-the-art application of Reinforcement learning inside Generative AI.
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By the end of the course, you’ll go from:
❌ “I know what RL is, but not how to use it…”
✅ “I’m using RL to optimize real business decisions.”
Who is this course for
01
Data Scientists & ML Engineers:
Go beyond predictions—learn to build RL model that make dynamic business decisions using real-world data.
02
Operations Research & Applied Scientists
Bridge OR and RL—apply decision-making to complex, real-world business problems.
03
Researchers (PhDs & Academics)
Turn RL theory into action—gain hands-on skills to build business-ready RL applications.
What you’ll get out of this course
Master Practical Reinforcement Learning
Learn the key RL tools—MDPs, Q-learning—and how to balance exploration vs. exploitation, all with clear, step-by-step Python builds you can rerun on your own data.
Tackle High-Value Business Use Cases
In live sessions we’ll map real-world problems—dynamic pricing, stock replenishment, sell-or-hold exits—to sequential decision analytics, to arrive high value decisions.
Apply RL to business problems like inventory management and dynamic pricing
So you can move from theory to practice in areas where decisions compound over time.
Experiment with cutting-edge techniques like Reinforcement Learning from Human Feedback (RLHF)
You’ll leave with insight into how human values can be integrated into reinforcement learning algorithm.
Hands-On Exercises, Examples and Code
I will provide end-to-end exercises, examples and code to make sure you come away with the skills you need. We will NOT just throw a bunch of slides at you!
Gain insight into Decision Intelligence
Learn how to combine data, business context, and AI to choose better actions and clearly explain the “why” behind each decision.
Weekly 1-hour office hour
Get personalized, live help each week to troubleshoot code, refine ideas, and make sure you’re on track.
This course includes
9 interactive live sessions
Lifetime access to course materials
6 in-depth lessons
Direct access to instructor
1 projects to apply learning
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Applied Reinforcement Learning for Business Decision Optimization
Course syllabus
Week 1
May
15
Session 1: Introduction, Sequential Decision Analytics 101
[Case Study 1]: Sequential Thinking in Action
May
16
Office Hours
Week 2
May
22
Session 2: Sequential Decision Analytics - The Power of Simple Policies
May
23
Office Hours
[Case Study 2]: Sell or Hold? Sequential Decision-Making in Financial Markets
Week 3
May
29
Session 3: Introduction to Reinforcement Learning
May
30
Office Hours
[Case Study 3]: The Grid Explorer: Q-Learning from Scratch
Week 4
Jun
3
Optional: Session 4: Reinforcement learning fro Optimizing Inventory Management
Jun
4
Optional: Session 5: Reinforcement learning for Dynamic Pricing
Jun
5
Optional: Session 6: Reinforcement Learning from Human Feedback
[Case Study 4]: Inventory management with Reinforcement Learning
[Case Study 5]: Dynamic Pricing with Reinforcement Learning
[Case Study 6]: Reinforcement learning from Human Feedback
Instructor
Peyman Kor
PhD in Reinforcement leaning | +5 years in Data & AI
Peyman Kor is a PhD candidate in Reinforcement Learning with a strong focus on Sequential Decision Analytics. For the past five years, he has been developing decision analytics & RL models for real-world decision-making challenges in areas like finance, supply chains, and energy.
Peyman has authored over 20 in-depth blogs on reinforcement learning and decision analytics, making complex topics practical and accessible to thousands of readers. His academic research has resulted in multiple peer-reviewed publications.
His mission is to make advanced decision science accessible, actionable, and directly applicable to business and technology challenges.
Course schedule
4-6 hours per week
Thursdays
16:00pm - 17:30pm CET
If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students
Weekly projects
2 hours per week
Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
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Applied Reinforcement Learning for Business Decision Optimization
Cohort 1
$500
Dates
Payment Deadline