Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes are uncertain. [1]

Jul 5, 2024 · A Markov Decision Process (MDP) model contains: A set of possible world states S. A set of Models. A set of possible actions A. A real-valued reward function R(s,a). A policy is a solution to Markov Decision Process. What is a State? A State is a set of tokens that represent every state that the agent can be in. What is a Model?

Aug 13, 2024 · A Markov decision process (MDP) is a stochastic (randomly-determined) mathematical tool based on the Markov property concept. It is used to model decision-making problems where outcomes are partially random and partially controllable, and to help make optimal decisions within a dynamic system.

Mar 10, 2025 · A Markov decision process (MDP) is defined as a stochastic decision-making process that uses a mathematical framework to model the decision-making of a dynamic system in scenarios where the results are either random or controlled by a decision maker, which makes sequential decisions over time.

Feb 24, 2025 · MDPs provide a formalism for modeling decision-making in situations where outcomes are uncertain, making them essential for reinforcement learning. An MDP is defined by a tuple (S, A, P, R, \gamma) (S,A,P,R,γ) where: S (State Space): A finite or infinite set of states representing the environment.

Dec 21, 2020 · MDPs can be characterized as both finite or infinite and continuous or discrete depending on the set of actions and states available and the decision making frequency. This article will focus on discrete MDPs with finite states and finite actions for the sake of simplified calculations and numerical examples.

A Markov decision process (MDP) is a Markov reward process with decisions. It is an environment in which all states are Markov. is a reward function, Ra s = E [Rt+1 j St is a discount factor 2 [0; 1]. The optimal value function speci es the best possible performance in the MDP.

Nov 5, 2024 · Markov Decision Processes (MDPs) are the mathematical backbone of Reinforcement Learning (RL), framing the structure for an agent to make decisions over time to maximize its reward. They...

Feb 6, 2025 · What is a Markov Decision Process? A Markov Decision Process (MDP) is a mathematical framework used to make decisions in situations where outcomes are uncertain. It is widely used in Artificial Intelligence (AI), robotics, economics, and even video games.

The abbreviation MDP commonly refers to Markov Decision Process, a mathematical framework used for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. This process is critical in fields such as reinforcement learning, robotics, and operations research.