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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 ...

We consider a receding horizon approach as an approximate solution totwo-person zero-sum Markov games with infinite horizon discounted costand average cost criteria. <p>We first present error bounds from the optimalequilibrium ...

The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to ...

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 ...

Based on recent results for multi-armed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite horizon Markov decision process (MDP) with infinite state space but finite ...

We propose a novel algorithm called Evolutionary Policy Iteration (EPI) for solving infinite horizon discounted reward Markov Decision Process (MDP) problems. EPI inherits the spirit of the well-known PI algorithm but ...