Multitask Soft Option Learning

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth, Wendelin Böhmer, Shimon Whiteson

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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We present Multitask Soft Option Learning (MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by a shared prior. This “soft” version of options avoids several instabilities during training in a multitask setting, and provides a natural way to learn both intra-option policies and their terminations. Furthermore, it allows fine-tuning of options for new tasks without forgetting their learned policies, leading to faster training without reducing the expressiveness of the hierarchical policy. We demonstrate empirically that MSOL significantly outperforms both hierarchical and flat transfer-learning baselines.
Original languageEnglish
Title of host publicationProceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
Number of pages10
Publication statusPublished - 2020
Event36th Conference on Uncertainty in Artificial Intelligence - Virtual/online event
Duration: 4 Aug 20206 Aug 2020
Conference number: 36

Publication series

NameProceedings of Machine Learning Research


Conference36th Conference on Uncertainty in Artificial Intelligence
Abbreviated titleUAI 2020


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