Now showing items 1-7 of 7
Simulation-Based Approach for Semiconductor Fab-Level Decision Making - Implementation Issues
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 ...
Markov Games: Receding Horizon Approach
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 ...
Risk-Sensitive Probability for Markov Chains
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 ...
Approximate Policy Iteration for Semiconductor Fab-Level Decision Making - a Case Study
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 ...
An Adaptive Sampling Algorithm for Solving Markov Decision Processes
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 ...
Evolutionary Policy Iteration for Solving Markov Decision Processes
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 ...