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Markov Decision Processes

[ˈmɑːkɒf dɪˈsɪʒən ˈprəʊsesɪz]
Machine Learning
Last updated: December 9, 2024

Definition

A mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.

Detailed Explanation

MDPs provide a formal framework for decision making and planning with probabilistic outcomes. They are defined by a set of states, actions, transition probabilities between states, and rewards. The key property is the Markov property, which states that the future only depends on the current state, not the history of how you got there.

Use Cases

Robot navigation, game AI, resource allocation, maintenance scheduling, portfolio management

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