Diverse, Top-k, and Top-Quality Planning Over Simulators
This enables planning in simulation-based domains like path-planning with hidden information, though it is incremental as it adapts existing MCTS methods.
The paper tackles the problem of generating diverse, top-k, and top-quality plans for sequential decision problems where only a black-box simulator is available, using Monte Carlo Tree Search (MCTS) to produce plan sets in domains inaccessible to classical planners.
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.