Bokus

Aaron M. Roth – författare

Visar alla böcker från författaren Aaron M. Roth. Handla med fri frakt och snabb leverans.

4 produkter

  1. William Leisner, Aaron M. Roth - Second Round: A Return to the Ur-Bar, Häftad. Tillgänglighet: Lägg i varukorg

    Second Round: A Return to the Ur-Bar

    Av William Leisner, Aaron M. Roth

    Häftad, 2018

    304 kr

    Lägg i varukorg

  2. Aaron M. Roth, Dinesh Manocha, Ram D. Sriram, Elham Tabassi - Explainable and Interpretable Reinforcement Learning for Robotics, Inbunden. Tillgänglighet: Lägg i varukorg

    Explainable and Interpretable Reinforcement Learning for Robotics

    Av Aaron M. Roth, Dinesh Manocha m. fl.

    Inbunden, 2024

    607 kr

    Lägg i varukorg

    This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for …

  3. Elham Tabassi, Ram D. Sriram, Dinesh Manocha, Aaron M. Roth - Explainable and Interpretable Reinforcement Learning for Robotics, E-bok. Tillgänglighet: Lägg i varukorg

    Explainable and Interpretable Reinforcement Learning for Robotics

    Av Elham Tabassi, Ram D. Sriram m. fl.

    E-bok, 2024

    815 kr

    Lägg i varukorg

    This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for …

  4. Aaron M. Roth, Dinesh Manocha, Ram D. Sriram, Elham Tabassi - Explainable and Interpretable Reinforcement Learning for Robotics, Häftad. Tillgänglighet: Lägg i varukorg

    Explainable and Interpretable Reinforcement Learning for Robotics

    Av Aaron M. Roth, Dinesh Manocha m. fl.

    Häftad, 2025

    607 kr

    Lägg i varukorg

    This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for …