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. [1]
References
- ^ Thrun, S.; Burgard, W.; Fox, D. (2005). Probabilistic Robotics. Cambridge: The MIT Press. ISBN 0-262-20162-3.
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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. [1]
References
- ^ Thrun, S.; Burgard, W.; Fox, D. (2005). Probabilistic Robotics. Cambridge: The MIT Press. ISBN 0-262-20162-3.
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| This robotics-related article is a stub. You can help Wikipedia by expanding it. |