"A Simulation-based Approach to Stochastic Dynamic Programming"
Applied Stochastic Models in Business and Industry,
Volume: 27 | Issue: 2 | Pages: 151-163
Publication type: Journal article
In this paper we develop a simulation-based approach to stochastic dynamic programming. To solve the Bellman equation we construct Monte Carlo estimates of Q-values. Our method is scalable to high dimensions and works in both continuous and discrete state and decision spaces whilst avoiding discretization errors that plague traditional methods. We provide a geometric convergence rate. We illustrate our methodology with a dynamic stochastic investment problem.
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