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Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study

In this paper, we propose an approximate policy iteration (API) algorithm for a semiconductor fab-level decision making problem. This problem is formulated as a discounted cost Markov Decision Process (MDP), and we have applied exact policy iteration to solve a simple example in prior work [1]. However, the overwhelming computational requirements of exact policy iteration prevent its application for larger problems. Approximate policy iteration overcomes this obstacle by approximating the cost-to-go using function approximation. Numerical simulation on the same example shows that the proposed API algorithm leads to a policy with cost close to that of the optimal policy.