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Constant-Factor Approximation Algorithms for Stochastic Control

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If you have a question about this talk, please contact Dr Guy-Bart Stan.

For many control problems, computation of the optimal controller is intractable. Our objective in this research is to develop simple algorithms for computing approximately optimal polices, and show that the resulting cost achieved is close to the optimal achievable cost.

We present a a simple way to compute upper and lower bounds on the performance of stochastic control systems. We consider Markov decision processes over general state spaces, and our approach allows any function to be used as an approximate Hamilton-Jacobi solution.

We give a number of examples including event-based sampling, dynamic planning for multiple vehicles, decentralized decision problems and queuing. For each of these we construct a decentralized policy and give a bound on the ratio of the cost achieved to the optimal achievable cost.

This talk is part of the CUED Control Group Seminars series.

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