A common paradigm in classical planning is heuristic forward
search. Forward search planners often rely on simple
best-first search which remains fixed throughout the search
process. In this paper, we introduce a novel search framework
capable of alternating between several forward search
approaches while solving a particular planning problem. Selection
of the approach is performed using a trainable stochastic
policy, mapping the state of the search to a probability distribution
over the approaches. This enables using policy gradient
to learn search strategies tailored to a specific distributions
of planning problems and a selected performance metric,
e.g. the IPC score. We instantiate the framework by constructing
a policy space consisting of five search approaches
and a two-dimensional representation of the planner’s state.
Then, we train the system on randomly generated problems
from five IPC domains using three different performance metrics.
Our experimental results show that the learner is able
to discover domain-specific search strategies, improving the
planner’s performance relative to the baselines of plain bestfirst
search and a uniform policy.