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

GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations

GePASE: Generalized Edge-Based Parallel A for Slow Evaluations