Markov Decision Process (MDP)

A Markov Decision Process (MDP) is a mathematical model used to represent decision-making problems in environments where outcomes depend both on current actions and on a certain degree of randomness.

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MDPs form a fundamental basis for many reinforcement learning (RL) algorithms, which enable artificial intelligence to learn how to act in complex and dynamic environments.

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What is an MDP?

An MDP is defined by:

• A set of states (S) describing all possible situations;
• A set of actions (A) that the agent can take;
• A transition function (P) that gives the probability of moving from one state to another depending on the chosen action;
• A reward function (R) that assigns a numerical value to each action, depending on its outcome.

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The objective is to determine an optimal policy (π) that maximizes the expected cumulative rewards over time.

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Practical applications of MDPs

MDPs are widely used in AI applications such as:

• Autonomous robots learning to move in uncertain environments;
• Recommendation systems, which adjust their suggestions based on user behavior;
• Resource management (e.g., energy, computer networks), where decisions must take into account constraints and risks.

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MDPs and datasets

The effectiveness of models based on MDPs strongly depends on the data used to train RL algorithms. High-quality annotated datasets are essential to correctly define states, actions, and rewards.

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That is why experts such as Innovatiana support companies in the creation of specialized datasets for reinforcement learning.

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👉 Learn more:

• Text annotation
• Multimodal annotation

Academic references

• Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley.
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
• Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research, 4, 237–285.