Markov Models
Machine Learning
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
Markov Decision Processes (MDPs) are mathematical frameworks used to model decision-making where outcomes depend on both probabilistic events and the choices made by an agent. They consist of states, actions, transition probabilities, and rewards, enabling the formulation of strategies to maximize cumulative rewards over time. MDPs are fundamental in reinforcement learning for developing optimal policies.
Use Cases
- •Optimizing robot navigation by determining best paths under uncertainty using Markov Decision Processes.
Related Terms
Other terms in the Machine Learning category