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The "Markov" in "Markov decision process" refers to the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making decisions that influence these state transitions, extending the concept of a Markov chain into the realm of decision-making under uncertainty.
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.
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), [1] which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward ...
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.It was proposed by Rummery and Niranjan in a technical note [1] with the name "Modified Connectionist Q-Learning" (MCQ-L).
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 ingredients of a stochastic game are: a finite set of players ; a state space (either a finite set or a measurable space (,)); for each player , an action set (either a finite set or a measurable space (,)); a transition probability from , where = is the action profiles, to , where (,) is the probability that the next state is in given the current state and the current action profile ; and ...
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.
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 ...