Abstract
We propose an approach to general subgoal-based
temporal abstraction in MCTS. Our approach approximates a set of available macro-actions locally
for each state only requiring a generative model and
a subgoal predicate. For that, we modify the expansion step of MCTS to automatically discover
and optimize macro-actions that lead to subgoals.
We empirically evaluate the effectiveness, computational efficiency and robustness of our approach
w.r.t. different parameter settings in two benchmark
domains and compare the results to standard MCTS
without temporal abstraction