---
title: "Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search"
authors:
  - "Thomas Gabor"
  - "Jan Peter"
  - "Thomy Phan"
  - "Christian Meyer"
  - "Claudia Linnhoff-Popien"
doi: "10.24963/ijcai.2019/772"
url: "https://ijcai.org/proceedings/2019/772"
pdf: "https://ijcai.org/proceedings/2019/0772.pdf"
venue: "Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"
year: 2019
pages: "5562-5568"
track: "Main track"
keywords:
  - "Planning and Scheduling: Temporal and Hybrid planning"
  - "Planning and Scheduling: Planning Algorithms"
  - "Planning and Scheduling: Hierarchical planning"
---

# Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search

Thomas Gabor, Jan Peter, Thomy Phan, Christian Meyer, Claudia Linnhoff-Popien

*Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence*, pages 5562-5568. DOI: [10.24963/ijcai.2019/772](https://doi.org/10.24963/ijcai.2019/772)

## 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.