Description

Book Synopsis
Integral and Inverse Reinforcement Learning for Optimal Control Systems and Gamesdevelops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverseoptimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains aircraft, robotics, power systems, and communication networks among them with theoretical insights valuable in tackling the real-world challenges they face.

Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games

    Product form

    £107.99

    Includes FREE delivery

    RRP £119.99 – you save £12.00 (10%)

    Order before 4pm today for delivery by Fri 3 Jul 2026.

    A Hardback by Bahare Kiumarsi

    1 in stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games by Bahare Kiumarsi

      Publisher: Springer International Publishing AG
      Publication Date: 1/6/2024
      ISBN13: 9783031452512, 978-3031452512
      ISBN10: 3031452518
      Also in:
      Automatic control engineering

      Description

      Book Synopsis
      Integral and Inverse Reinforcement Learning for Optimal Control Systems and Gamesdevelops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverseoptimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains aircraft, robotics, power systems, and communication networks among them with theoretical insights valuable in tackling the real-world challenges they face.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account