Data-driven optimal control with a relaxed linear program

نویسندگان

چکیده

The linear programming (LP) approach has a long history in the theory of approximate dynamic programming. When it comes to computation, however, LP often suffers from poor scalability. In this work, we introduce relaxed version Bellman operator for q-functions and prove that is still monotone contraction mapping with unique fixed point. spirit approach, exploit new build program (RLP). Compared standard formulation, our RLP only one family constraints half decision variables, making more scalable computationally efficient. For deterministic systems, trivially returns correct q-function. stochastic systems continuous spaces, solution preserves minimizer optimal q-function, hence retrieves policy. Theoretical results are backed up simulation where solve sampled versions LPs data collected by interacting environment. general nonlinear observe again tends preserve minimizers LP, though relative performance influenced specific geometry problem.

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ژورنال

عنوان ژورنال: Automatica

سال: 2022

ISSN: ['1873-2836', '0005-1098']

DOI: https://doi.org/10.1016/j.automatica.2021.110052