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A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal · Elad Hazan · Anirudha Majumdar · Karan Singh
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.
Author Information
Naman Agarwal (Google Research)
Elad Hazan (Princeton University and Google Brain)
Anirudha Majumdar (Princeton University)
Karan Singh (Microsoft Research)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: A Regret Minimization Approach to Iterative Learning Control »
Wed. Jul 21st 04:00 -- 06:00 PM Room Virtual
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