A method of providing a reinforcement learning, RL, agent for decision-making to be used in controlling an autonomous vehicle. The method includes: a plurality of training sessions, in which the RL agent interacts with a first environment including the autonomous vehicle, each training session having a different initial value and yielding a state-action value function Qk(s, a) dependent on state and action; an uncertainty evaluation on the basis of a variability measure for the plurality of state-action value functions evaluated for one or more state-action pairs corresponding to possible decisions by the trained RL agent; additional training, in which the RL agent interacts with a second environment including the autonomous vehicle, wherein the second environment differs from the first environment by an increased exposure to a subset of state-action pairs for which the variability measure indicates a relatively higher uncertainty.


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    Title :

    UNCERTAINTY-DIRECTED TRAINING OF A REINFORCEMENT LEARNING AGENT FOR TACTICAL DECISION-MAKING


    Contributors:
    HOEL CARL-JOHAN (author) / LAINE LEO (author)

    Publication date :

    2023-08-03


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion





    TACTICAL DECISION-MAKING IN AUTONOMOUS DRIVING BY REINFORCEMENT LEARNING WITH UNCERTAINTY ESTIMATION

    Hoel, Carl-Johan / Wolff, Krister / Laine, Leo | British Library Conference Proceedings | 2020


    Tactical Decision Making

    McKinney, D. / Flight Safety Foundation / International Federation of Airworthiness et al. | British Library Conference Proceedings | 2000


    Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

    Hoel, Carl-Johan / Driggs-Campbell, Katherine / Wolff, Krister et al. | IEEE | 2020