Essays about: "Djup förstärkande inlärning"

Found 3 essays containing the words Djup förstärkande inlärning.

  1. 1. Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Victor Sanchez; [2023]
    Keywords : Deep Reinforcement Learning; Environment-aware navigation; Robotics; Artificial Intelligence; Apprentissage par renforcement profond; Navigation consciente de l’humain; Intelligence Artificielle; Robotique; Djup Förstärkande Inlärning; Människomedveten navigering; Robotik; Artificiell Intelligens;

    Abstract : Reinforcement learning (RL) has seen a fast expansion in recent years of its successful application to a range of decision-making and complex control tasks. Moreover, deep learning offers RL the opportunity to enlarge its spectrum of complex fields. READ MORE

  2. 2. The effects of multistep learning in the hard-exploration problem

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jacob Friman; [2022]
    Keywords : ;

    Abstract : Reinforcement learning is a machine learning field which has received revitalised interest in later years due to many success stories and advancements in deep reinforcement learning. A key part in reinforcement learning is the need for exploration of the environment so the agent can properly learn the best policy. READ MORE

  3. 3. Deep Reinforcement Learning for Temperature Control in Buildings and Adversarial Attacks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Kevin Ammouri; [2021]
    Keywords : Deep Reinforcement Learning; Adversarial Attacks; Optimal Attacks; Building Control; Optimal Control; Energy Efficiency; Djup förstärkande inlärning; Adversarial Attacker; Optimala Attacker; Byggnadskontroll; Optimal Kontroll; Energieffektivitet;

    Abstract : Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are energy consuming and traditional methods used for building control results in energy losses. The methods cannot account for non-linear dependencies in the thermal behaviour. READ MORE