Concepedia

Concept

markov decision processes

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About

Markov decision processes is a mathematical framework for modeling sequential decision-making in stochastic environments. It provides a rigorous method for analyzing situations where outcomes are probabilistic and influenced by chosen actions. This framework characterizes dynamic systems by states, available actions, state transition probabilities, and associated rewards, adhering to the Markov property where the next state depends solely on the current state and action. Its significance lies in enabling the computation of optimal policies aimed at maximizing cumulative expected rewards, making it fundamental in fields like reinforcement learning, control theory, and operations research.

Top Authors

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XG

Sun Yat-sen University

SM

Technion – Israel Institute of Technology

RL

Colorado State University

AH

Leiden University

EB

Cornell University

Top Institutions

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Pittsburgh, United States

Stanford University

Stanford, United States

University of Massachusetts Amherst

Amherst Center, United States