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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 ] Originating from operations research in the 1950s, [ 2 ] [ 3 ] MDPs have since gained recognition in a variety of fields, including ecology , economics , healthcare ...
A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state.
The optimization problem follows a Markov decision process The states x t {\displaystyle x_{t}} follow a Markov chain . That is, attainment of state x t {\displaystyle x_{t}} depends only on the state x t − 1 {\displaystyle x_{t-1}} and not x t − 2 {\displaystyle x_{t-2}} or any prior state.
A Markov decision process is a Markov chain in which state transitions depend on the current state and an action vector that is applied to the system. Typically, a Markov decision process is used to compute a policy of actions that will maximize some utility with respect to expected rewards.
Discrete-time Markov decision processes (MDP) are planning problems with: durationless actions, nondeterministic actions with probabilities, full observability, maximization of a reward function, and a single agent. When full observability is replaced by partial observability, planning corresponds to a partially observable Markov decision ...
The decentralized partially observable Markov decision process (Dec-POMDP) [1] [2] is a model for coordination and decision-making among multiple agents. It is a probabilistic model that can consider uncertainty in outcomes, sensors and communication (i.e., costly, delayed, noisy or nonexistent communication).
He pioneered the policy iteration method for solving Markov decision problems, and this method is sometimes called the "Howard policy-improvement algorithm" in his honor. [9] He was also instrumental in the development of the Influence diagram for the graphical analysis of decision situations.
Here are some of the more commonly known problems that are PSPACE-complete when expressed as ... (Partially Observable Markov Decision Processes). [50] Hidden Model ...