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MDPs in Stochastic Control Theory by achm3dz is a document available to read on EtoBox.

1. Markov decision processes (MDPs) provide a simple model for stochastic control systems, where the next state depends on the current state and control input, plus random noise. 2. For an MDP, it is shown that the optimal control strategy can depend only on the current state, not full history. Such a strategy is called a Markov strategy. 3. Dynamic programming can be used to recursively compute the optimal Markov strategy by defining and updating cost-to-go functions associated with each state. The opt

Author

achm3dz

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EN