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Method for solving stochastic control problems of linear systems in high dimension
Method for solving stochastic control problems of linear systems in high dimension
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机译:高维线性系统随机控制问题的求解方法
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摘要
Stochastic control problems of linear systems in high dimensions are solved by modeling a structured Markov Decision Process (MDP). A state space for the MDP is a polyhedron in a Euclidean space and one or more actions that are feasible in a state of the state space are linearly constrained with respect to the state. One or more approximations are built from above and from below to a value function for the state using representations that facilitate the computation of approximately optimal actions at any given state by linear programming.
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