AI-Driven Decision Making: Comparing Markov Decision Process and Reinforcement Learning

Author(s): Shenggang Li

Originally published on Towards AI.

How Trump and Harris Could Use MDP and RL to Maximize Their Chances in the 2024 Election

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Markov Decision Processes (MDP) and Reinforcement Learning (RL) are powerful tools for making decisions over time. They help determine the best actions under uncertainty, like when to buy or sell stocks, how to allocate resources in promotion sales for retail, or even election campaign decision-making. Despite their similarities, the two methods are often confused. This paper aims to clarify their distinctions and applications.

I’ll use the 2024 U.S. election as an example to show how MDP and RL work. Imagine Trump or Harris deciding whether to campaign in key states like Pennsylvania. How do they balance the costs and potential rewards?

I will explain how MDP employs a structured, mathematical framework, while Reinforcement Learning relies on trial-and-error learning. We’ll see how these methods approach decisions differently, yet both aim to maximize rewards in the long run.

This paper will also explain when to use each method, with examples from real-world situations like stock trading, retail promotion strategies, or political campaigns. You’ll learn how to improve and adapt these methods for different scenarios.

By the end, you’ll deeply understand how MDP and RL work when to apply them, and… Read the full blog for free on Medium.

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Published via Towards AI