Robust model-based reinforcement learning using L1 adaptive control
Karumanchi, Sambhu Harimanas
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https://hdl.handle.net/2142/121362
Description
Title
Robust model-based reinforcement learning using L1 adaptive control
Author(s)
Karumanchi, Sambhu Harimanas
Issue Date
2023-07-17
Director of Research (if dissertation) or Advisor (if thesis)
Hovakimyan, Naira
Department of Study
Aerospace Engineering
Discipline
Aerospace Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Robust Adaptive Control
Reinforcement Learning
Abstract
We introduce L1-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach approximates the transition function along each trajectory with a control-affine model to generate a control input augmentation that perturbs the input produced by the underlying MBRL policy. The perturbation produced by the L1 adaptive control is designed to enhance the robustness of the system against uncertainties. Importantly, the proposed L1 adaptive control-based learning scheme is agnostic to the choice of MBRL algorithm and can be integrated seamlessly with many model-based RL systems in practice. The method exhibits superior performance and sample efficiency on multiple MuJoCo environments, both with and without system noise, as demonstrated by our numerical simulations.
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