Learn more about Markov Decision Processes in this introductory tutorial. Learn more about us at: nebulum.one/no-code-ai/

Discover how Markov Decision Processes (MDPs) power the smartest AI systems around us. From delivery robots navigating obstacles to GPS finding optimal routes, MDPs enable machines to make intelligent decisions under uncertainty.

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Learn the 5 core components—states, actions, transition probabilities, rewards, and discount factors—through simple examples that make complex math accessible. We explore how these algorithms balance immediate rewards with future consequences, creating systems that appear locally myopic but remain globally optimal. This isn't just engineering—it's philosophy in code, forcing us to examine how we make decisions while teaching machines to think strategically.

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Whether you're a complete beginner or experienced no-coder, this video will save you countless hours of frustration and help you build and design professional AI powered apps in no time.

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Chapters

0:46 Why is Markov Decision Process Important?
1:45 Deterministic vs Stochastic
4:12 States
5:01 Memoryless
5:37 Actions
8:21 Rewards
9:07 Discount Factor
9:52 Environment
10:34 Reinforcement Learning

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