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GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations (2301.10347v2)

Published 24 Jan 2023 in cs.RO and cs.AI

Abstract: Parallel search algorithms have been shown to improve planning speed by harnessing the multithreading capability of modern processors. One such algorithm PA*SE achieves this by parallelizing state expansions, whereas another algorithm ePA*SE achieves this by effectively parallelizing edge evaluations. ePA*SE targets domains in which the action space comprises actions with expensive but similar evaluation times. However, in a number of robotics domains, the action space is heterogenous in the computational effort required to evaluate the cost of an action and its outcome. Motivated by this, we introduce GePA*SE: Generalized Edge-based Parallel A* for Slow Evaluations, which generalizes the key ideas of PA*SE and ePA*SE i.e. parallelization of state expansions and edge evaluations respectively. This extends its applicability to domains that have actions requiring varying computational effort to evaluate them. The open-source code for GePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search

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Authors (2)
  1. Shohin Mukherjee (7 papers)
  2. Maxim Likhachev (64 papers)
Citations (2)

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