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Robust Policy Synthesis for Uncertain POMDPs via Convex Optimization
[article]
2020
arXiv
pre-print
We study the problem of policy synthesis for uncertain partially observable Markov decision processes (uPOMDPs). The transition probability function of uPOMDPs is only known to belong to a so-called uncertainty set, for instance in the form of probability intervals. Such a model arises when, for example, an agent operates under information limitation due to imperfect knowledge about the accuracy of its sensors. The goal is to compute a policy for the agent that is robust against all possible
arXiv:2001.08174v2
fatcat:jeig4nh7vvfrfbod6q5p55ey2a