Finding Options that Minimize Planning Time
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:31203129, 2019.
Abstract
We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maximum of valueiteration passes. We first show that the problem is $\NP$hard, even if the task is constrained to be deterministicâ€”the first such complexity result for option discovery. We then present the first polynomialtime boundedly suboptimal approximation algorithm for this setting, and empirically evaluate it against both the optimal options and a representative collection of heuristic approaches in simple gridbased domains.
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