Optimal Sensing via Multi-armed Bandit Relaxations in Mixed Observability Domains

IEEE International Conference on Robotics and Automation, pp. 4807-4812, 2016.

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Abstract:

Sequential decision making under uncertainty is studied in a mixed observability domain. The goal is to maximize the amount of information obtained on a partially observable stochastic process under constraints imposed by a fully observable internal state. An upper bound for the optimal value function is derived by relaxing constraints....More

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