# Combining Spatial and Temporal Abstraction in Planning for Better Generalization

**Authors:** Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio  
**arXiv:** [2310.00229](https://arxiv.org/abs/2310.00229)  
**Date:** 2023-09-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.00229v1/

## Abstract

Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning agent that utilizes spatial and temporal abstractions to generalize learned skills in novel situations. It automatically decomposes the task at hand into smaller-scale, more manageable subtasks and hence enables sparse decision-making and focuses its computation on the relevant parts of the environment. This relies on the definition of a high-level proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end using hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper's significant advantage in zero-shot generalization, compared to existing state-of-the-art hierarchical planning methods.