Essays about: "förstärkningsinlärning"

Showing result 1 - 5 of 85 essays containing the word förstärkningsinlärning.

  1. 1. Decreasing Training Time of Reinforcement Learning Agents for Remote Tilt Optimization using a Surrogate Neural Network Approximator

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

    Author : Jiaming Huang; [2023]
    Keywords : ;

    Abstract : One possible application of reinforcement learning in the telecommunication field is antenna tilt optimization. However, one of key challenges we face is that the use of handcrafted simulators as environments to provide information for agents is often time-consuming regarding training reinforcement learning agents. READ MORE

  2. 2. Explainable AI for Multi-Agent Control Problem

    University essay from Mälardalens universitet/Akademin för innovation, design och teknik

    Author : Hanna Prokopova; [2023]
    Keywords : ;

    Abstract : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. READ MORE

  3. 3. S-MARL: An Algorithm for Single-To-Multi-Agent Reinforcement Learning : Case Study: Formula 1 Race Strategies

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

    Author : Marinaro Davide; [2023]
    Keywords : Reinforcement Learning; Single-to-Multi-Agent; Learning Stability; Exploration-Exploitation trade-off; Race Strategy Optimization; Förstärkningsinlärning; Från en till flera agenter; Stabilitet vid inlärning; Utforskning-exploatering; Optimering av tävlingsstrategier;

    Abstract : A Multi-Agent System is a group of autonomous, intelligent, interacting agents sharing an environment that they observe through sensors, and upon which they act with actuators. The behaviors of these agents can be either defined upfront by programmers or learned by trial-and-error resorting to Reinforcement Learning. READ MORE

  4. 4. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

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

    Author : Shihang Zhou; [2023]
    Keywords : Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

    Abstract : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. READ MORE

  5. 5. A hierarchical neural network approach to learning sensor planning and control

    University essay from Uppsala universitet/Datorteknik

    Author : Nicke Löfwenberg; [2023]
    Keywords : sensor planning; hierarchical reinforcement learning; reinforcement learning; sensor control; camera control; sensorplanering; hierarkisk förstärkningsinlärning; förstärkningsinlärning; sensorkontroll; kamerakontroll;

    Abstract : The ability to search their environment is one of the most fundamental skills for any living creature. Visual search in particular is abundantly common for almost all animals. READ MORE