Monte Carlo POMDP
Encyclopedia
In the class of Markov decision process
algorithms, the Monte Carlo POMDP (MC-POMDP) is the particle filter
version for the partially observable Markov decision process
(POMDP) algorithm. In MC-POMDP, particles filters are used to update and approximate the beliefs, and the algorithm is applicable to continuous valued states, actions, and measurements.
Markov decision process
Markov decision processes , named after Andrey Markov, provide 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 a wide range of optimization problems solved via...
algorithms, the Monte Carlo POMDP (MC-POMDP) is the particle filter
Particle filter
In statistics, particle filters, also known as Sequential Monte Carlo methods , are sophisticated model estimation techniques based on simulation...
version for the partially observable Markov decision process
Partially observable Markov decision process
A Partially Observable Markov Decision Process is a generalization of a Markov Decision Process. 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...
(POMDP) algorithm. In MC-POMDP, particles filters are used to update and approximate the beliefs, and the algorithm is applicable to continuous valued states, actions, and measurements.