A Regret Minimization Approach to Iterative Learning Control

Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:100-109, 2021.

Abstract

We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new performance metric, planning regret, which replaces the standard stochastic uncertainty assumptions with worst case regret. Based on recent advances in non-stochastic control, we design a new iterative algorithm for minimizing planning regret that is more robust to model mismatch and uncertainty. We provide theoretical and empirical evidence that the proposed algorithm outperforms existing methods on several benchmarks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-agarwal21b, title = {A Regret Minimization Approach to Iterative Learning Control}, author = {Agarwal, Naman and Hazan, Elad and Majumdar, Anirudha and Singh, Karan}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {100--109}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/agarwal21b/agarwal21b.pdf}, url = {https://proceedings.mlr.press/v139/agarwal21b.html}, abstract = {We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new performance metric, planning regret, which replaces the standard stochastic uncertainty assumptions with worst case regret. Based on recent advances in non-stochastic control, we design a new iterative algorithm for minimizing planning regret that is more robust to model mismatch and uncertainty. We provide theoretical and empirical evidence that the proposed algorithm outperforms existing methods on several benchmarks.} }
Endnote
%0 Conference Paper %T A Regret Minimization Approach to Iterative Learning Control %A Naman Agarwal %A Elad Hazan %A Anirudha Majumdar %A Karan Singh %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-agarwal21b %I PMLR %P 100--109 %U https://proceedings.mlr.press/v139/agarwal21b.html %V 139 %X We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics. In this setting, we propose a new performance metric, planning regret, which replaces the standard stochastic uncertainty assumptions with worst case regret. Based on recent advances in non-stochastic control, we design a new iterative algorithm for minimizing planning regret that is more robust to model mismatch and uncertainty. We provide theoretical and empirical evidence that the proposed algorithm outperforms existing methods on several benchmarks.
APA
Agarwal, N., Hazan, E., Majumdar, A. & Singh, K.. (2021). A Regret Minimization Approach to Iterative Learning Control. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:100-109 Available from https://proceedings.mlr.press/v139/agarwal21b.html.

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