A machine learning model for reinforcement learning uses parameterized families of Markov decision processes (MDP) with latent variables. The system uses latent variables to improve ability of models to transfer knowledge and generalize to new tasks. Accordingly, trained machine learning based models are able to work in unseen environments or combinations of conditions/factors that the machine learning model was never trained on. For example, robots or self-driving vehicles based on the machine learning based models are robust to changing goals and are able to adapt to novel reward functions or tasks flexibly while being able to transfer knowledge about environments and agents to new tasks.


    Access

    Download


    Export, share and cite



    Title :

    MODEL BASED REINFORCEMENT LEARNING BASED ON GENERALIZED HIDDEN PARAMETER MARKOV DECISION PROCESSES



    Publication date :

    2020-11-26


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / 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




    Learning Profiles Based on Hierarchical Hidden Markov Model

    Galassi, U. / Giordana, A. / Saitta, L. et al. | British Library Conference Proceedings | 2005


    Hidden Markov Model-based population synthesis

    Saadi, Ismaïl / Mustafa, Ahmed / Teller, Jacques et al. | Elsevier | 2016


    Hidden Markov Model-based population synthesis

    Saadi, Ismaïl | Online Contents | 2016


    HIDDEN MARKOV MODEL PARAMETER ESTIMATION FOR MULTIPLE DIM TARGET DETECTION

    Shim, S.-W. / Won, D.-Y. / Tahk, M.-J. et al. | British Library Conference Proceedings | 2012