Learning Neural Search Policies for Classical Planning
  Heuristic forward search is currently the dominant paradigm in classical
planning. Forward search algorithms typically rely on a single, relatively
simple variation of best-first search and remain fixed throughout the process
of solving a planning problem. Existing work combining multiple search
techniques usually aims at supporting best-first search with an additional
exploratory mechanism, triggered using a handcrafted criterion. A notable
exception is very recent work which combines various search techniques using a
trainable policy. It is, however, confined to a discrete action space
comprising several fixed subroutines.
  In this paper, we introduce a parametrized search algorithm template which
combines various search techniques within a single routine. The template's
parameter space defines an infinite space of search algorithms, including,
among others, BFS, local and random search. We further introduce a neural
architecture for designating the values of the search parameters given the
state of the search. This enables expressing neural search policies that change
the values of the parameters as the search progresses. The policies can be
learned automatically, with the objective of maximizing the planner's
performance on a given distribution of planning problems. We consider a
training setting based on a stochastic optimization algorithm known as the
cross-entropy method (CEM). Experimental evaluation of our approach shows that
it is capable of finding effective distribution-specific search policies,
outperforming the relevant baselines.
