Unsupervised Learning of Temporal Abstractions with Slot-Based Transformers

Anand Gopalakrishnan, Kazuki Irie, Juergen Schmidhuber, Sjoerd van Steenkiste

Research output: Contribution to journalArticlepeer-review


The discovery of reusable subroutines simplifies decision making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in an unsupervised fashion through observing state-action trajectories gathered from executing a policy. However, a current limitation is that they process each trajectory in an entirely sequential manner, which prevents them from revising earlier decisions about subroutine boundary points in light of new incoming information. In this work, we propose slot-based transformer for temporal abstraction (SloTTAr), a fully parallel approach that integrates sequence processing transformers with a slot attention module to discover subroutines in an unsupervised fashion while leveraging adaptive computation for learning about the number of such subroutines solely based on their empirical distribution. We demonstrate how SloTTAr is capable of outperforming strong baselines in terms of boundary point discovery, even for sequences containing variable amounts of subroutines, while being up to seven times faster to train on existing benchmarks.
Original languageEnglish (US)
Pages (from-to)1-34
Number of pages34
JournalNeural Computation
StatePublished - Feb 2 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-02-09
Acknowledgements: We thank Aditya Ramesh, Aleksandar Stanic and Klaus Greff for useful discussions and ´ valuable feedback. The large majority of this research was funded by Swiss National Science Foundation grant: 200021 192356, project NEUSYM. This work was also supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID s1023 and s1154. We also thank NVIDIA Corporation for donating DGX machines as part of the Pioneers of AI Research Award.

ASJC Scopus subject areas

  • Cognitive Neuroscience


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