Multitask Soft Option Learning

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth, Wendelin Boehmer, Shimon Whiteson
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:969-978, 2020.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-igl20a, title = {Multitask Soft Option Learning}, author = {Igl, Maximilian and Gambardella, Andrew and He, Jinke and Nardelli, Nantas and Siddharth, N and Boehmer, Wendelin and Whiteson, Shimon}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {969--978}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/igl20a/igl20a.pdf}, url = {https://proceedings.mlr.press/v124/igl20a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Multitask Soft Option Learning %A Maximilian Igl %A Andrew Gambardella %A Jinke He %A Nantas Nardelli %A N Siddharth %A Wendelin Boehmer %A Shimon Whiteson %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-igl20a %I PMLR %P 969--978 %U https://proceedings.mlr.press/v124/igl20a.html %V 124 %X 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.
APA
Igl, M., Gambardella, A., He, J., Nardelli, N., Siddharth, N., Boehmer, W. & Whiteson, S.. (2020). Multitask Soft Option Learning. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:969-978 Available from https://proceedings.mlr.press/v124/igl20a.html.

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