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Authors: Daniel Nikovski 1 ; Junmin Zhong 1 ; 2 and William Yerazunis 1

Affiliations: 1 Mitsubishi Electric Research Labs, Massachusetts, U.S.A. ; 2 Arizona State University, Arizona, U.S.A.

Keyword(s): Learning Control, Differential Dynamic Programming, Value Function Approximation, Policy Learning.

Abstract: The paper proposes a novel method for constructing a global control policy, valid everywhere in the state space of a dynamical system, from a set of solutions computed for specific initial states in that space by means of differential dynamic programming. The global controller chooses controls based on elements of the pre-computed solutions, leveraging the property that these solutions compute not only nominal state and control trajectories from the initial states, but also a set of linear controllers that can stabilize the system around the nominal trajectories, as well as a set of localized estimators of the optimal cost-to-go for system states around the nominal states. An empirical verification of three variants of the algorithm on two benchmark problems demonstrates that making use of the cost-to-go estimators results in the best performance (lowest average cost) and often leads to dramatic reduction in the number of pre-computed solutions that have to be stored in memory, which in its turn speeds up control computation in real time. (More)

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Paper citation in several formats:
Nikovski, D.; Zhong, J. and Yerazunis, W. (2024). Memory-Based Learning of Global Control Policies from Local Controllers. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7; ISSN 2184-2809, SciTePress, pages 237-244. DOI: 10.5220/0012921900003822

@conference{icinco24,
author={Daniel Nikovski. and Junmin Zhong. and William Yerazunis.},
title={Memory-Based Learning of Global Control Policies from Local Controllers},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={237-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012921900003822},
isbn={978-989-758-717-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Memory-Based Learning of Global Control Policies from Local Controllers
SN - 978-989-758-717-7
IS - 2184-2809
AU - Nikovski, D.
AU - Zhong, J.
AU - Yerazunis, W.
PY - 2024
SP - 237
EP - 244
DO - 10.5220/0012921900003822
PB - SciTePress

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