Solving a Dynamic Program by Linear Programming — General State and Action Spaces
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In the past two decades it has been shown by various authors that stochastic dynamic programming problems with finite state and action spaces and different notions of optimality can be solved by linear programming. In the present lecture it will bedemonstrated that dynamic programs with general state and action spaces can be treated by a generalized linear programming approach in the discounted reward case, in the average reward case, and in the optimal stopping case.
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