Markov decision process Guide, Meaning , Facts, Information and Description

A Markov Decision Process (MDP) is a discrete time stochastic control process characterized by a set of states, actions, and transition probability matrices that depend on the actions chosen within a given state.

MDPs are useful for studying a wide range of optimization problems solved via dynamic programming and reinforcement learning.

References

• Bellman, R. E. Dynamic Programming. Princeton University Press, Princeton, NJ.
• M. L. Puterman. Markov Decision Processes. Wiley, 1994.

External links

• MDP Toolbox for Matlab - An excellent tutorial and Matlab toolbox for working with MDPs.

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