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A Regret Minimization Approach to Iterative Learning Control
Naman Agarwal · Elad Hazan · Anirudha Majumdar · Karan Singh

Wed Jul 21 06:35 AM -- 06:40 AM (PDT) @
in Reinforcement Learning 12 »

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.

Keywords: Planning and Control · Reinforcement Learning and Planning

Author Information

Naman Agarwal (Google Research)
Elad Hazan (Princeton University and Google Brain)
Anirudha Majumdar (Princeton University)

Anirudha Majumdar is an Assistant Professor in the Mechanical and Aerospace Engineering (MAE) department at Princeton University. He also holds a part-time visiting research scientist position at the Google AI Lab in Princeton. Majumdar received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. He is a recipient of the Sloan Fellowship, ONR Young Investigator Program (YIP) award, the NSF CAREER award, the Google Faculty Research Award (twice), the Amazon Research Award (twice), the Young Faculty Researcher Award from the Toyota Research Institute, the Paper of the Year Award from the International Journal of Robotics Research (IJRR), the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA), the Alfred Rheinstein Faculty Award (Princeton), and the Excellence in Teaching Award (Princeton SEAS).

Karan Singh (Microsoft Research)

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