Also known as: Markov decision process (MDP) Β· MDP
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making where outcomes are partly random and partly controlled by an agent's actions. It represents states, actions, transitions, and rewards under the Markov property. MDPs are central to reinforcement learning, dynamic programming, and stochastic control.
A discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning.
A graph representing the decision-making model where decisions (or actions) are taken to navigate a sequence of states under the assumption that the Markov property holds. In reinforcement learning, these transitions between states return a numerical reward.