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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/6142

Title: Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study
Authors: He, Ying
Bhatnagar, Shalabh
Fu, Michael C.
Marcus, Steven I.
Advisors: Marcus, Steven I.
Department/Program: ISR
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.
URI: http://hdl.handle.net/1903/6142
Appears in Collections:Institute for Systems Research Technical Reports

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