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|Title: ||Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study|
|Authors: ||He, Ying|
Fu, Michael C.
Marcus, Steven I.
|Advisors: ||Marcus, Steven I.|
|Type: ||Technical Report|
|Keywords: ||Approximate Policy Iteration, Semiconductor Fab-Level Decision Making, Markov Decision Processes, Discounted Cost Problem, Sensor-Actuator Networks, Next-Generation Product Realization Systems|
|Issue Date: ||2000|
|Series/Report no.: ||ISR; TR 2000-49|
|Abstract: ||In this paper, we propose an approximate policy iteration (API) algorithm for asemiconductor fab-level decision making problem. This problem is formulated as adiscounted cost Markov Decision Process (MDP), and we have applied exact policy iterationto solve a simple example in prior work. However, the overwhelmingcomputational requirements of exact policy iteration prevent its application forlarger problems. Approximate policy iteration overcomes this obstacle by approximating thecost-to-go using function approximation. Numerical simulation on the same example showsthat the proposed API algorithm leads to a policy with cost close to that of the optimalpolicy.|
|Appears in Collections:||Institute for Systems Research Technical Reports|
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