We present a new reinforcement learning system more suitable to be used in robotics than existing ones. Existing reinforcement learning algorithms are not specifically tailored for robotics and so they do not take advantage of the robotic perception characteristics as well as of the expected complexity of the task that robots are likely to face. In a robot, the information about the environment comes from a set of qualitatively different sensors and in the main par of tasks small subsets of these sensors provide enough information to correctly predict the effect of actions. Departing from this analysis, we outline a new reinforcement learning system that aims at determining relevant subsets of sensors for each action and we present an algorithm that partially implements this new reinforcement learning architecture. Results of the application of the algorithm to the problem of learning to walk with a six legged robot are presented and compared with a well known reinforcement learning algorithm (Q-learning) showing the advantages of our approach.


    Access

    Download


    Export, share and cite



    Title :

    Rho-learning: a robotics oriented reinforcement learning algorithm


    Contributors:

    Publication date :

    2000-01-01


    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    RL-Studio: A Tool for Reinforcement Learning Methods in Robotics

    Fernández de Cabo, Pedro / Lucas, Rubén / Arranz, Ignacio et al. | Springer Verlag | 2022



    Lunar Lander Using Reinforcement Learning Algorithm

    Mali, Rohan / Kande, Nikhil / Mandwade, Sangam et al. | IEEE | 2023


    Learning robotics: a review

    Ribeiro, A. Fernando / Lopes, Gil | BASE | 2020

    Free access

    IRLSOT: Inverse reinforcement learning for scene‐oriented trajectory prediction

    He, Caizhen / Chen, Lanping / Xu, Liming et al. | Wiley | 2022

    Free access