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Approximate Dynamic Programming (ADP)

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Encyclopedia of Systems and Control

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Abstract

Approximate dynamic programming (ADP or RLADP) includes a wide variety of general methods to solve for optimal decision and control in the face of complexity, nonlinearity, stochasticity, and/or partial observability. This entry first reviews methods and a few key applications across decision and control engineering (e.g., vehicle and logistics control), computer science (e.g., AlphaGo), operations research, and connections to economics, neuropsychology, and animal behavior. Then it summarizes a sixfold mathematical taxonomy of methods in use today, with pointers to the future.

Paul J. Werbos has retired.

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Bibliography

  • Lewis FL, Liu D (eds), (2013) Reinforcement learning and approximate dynamic programming for feedback control, vol 17. Wiley (IEEE Series), New York

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Author information

Authors and Affiliations

  1. National Science Foundation, Arlington, VA, USA

    Paul J. Werbos

Authors
  1. Paul J. Werbos
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Correspondence to Paul J. Werbos .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, MA, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, MN, USA

    Tariq Samad

Section Editor information

  1. South Kensington Campus, Electrical and Electronic Engineering, Imperial College, London, United Kingdom

    Thomas Parisini

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Werbos, P.J. (2020). Approximate Dynamic Programming (ADP). In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100096-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_100096-1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5102-9

  • Online ISBN: 978-1-4471-5102-9

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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