Institute for Systems Research

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Author: search.filters.author.He, YingSubject: search.filters.subject.Markov Decision Processes

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    Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study
    (2000) He, Ying; Bhatnagar, Shalabh; Fu, Michael C.; Marcus, Steven I.; Marcus, Steven I.; ISR
    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.
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    Simulation-Based Approach for Semiconductor Fab-Level Decision Making - Implementation Issues
    (2000) He, Ying; Fu, Michael C.; Marcus, Steven I.; Marcus, Steven I.; ISR
    In this paper, we discuss implementation issues of applying a simulation-based approach to asemiconductor fab-level decision making problem. The fab-level decision making problem isformulated as a Markov Decision Process (MDP). We intend to use a simulation-based approach sinceit can break the "curse of dimensionality" and the "curse of modeling" for an MDP with largestate and control spaces. We focus on how to parameterize the state space and the control space.
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    Simulation-Based Algorithms for Average Cost Markov Decision Processes
    (1999) He, Ying; Fu, Michael C.; Marcus, Steven I.; Fu, Michael C.; Marcus, Steven I.; ISR
    In this paper, we give a summary of recent development of simulation-based algorithmsfor average cost MDP problems, which are different from those for discounted cost problems or shortest pathproblems. We introduce both simulation-based policy iteration algorithms and simulation-based value iterationalgorithms for average cost problems, and give the pros and cons of each algorithm.