Sökning: "Säker Förstärkningsinlärning"

Hittade 4 uppsatser innehållade orden Säker Förstärkningsinlärning.

1. 1. Safe Reinforcement Learning for Social Human-Robot Interaction : Shielding for Appropriate Backchanneling Behavior

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mohamed Akif; [2023]
   Nyckelord :Human-Robot Interaction; Backchanneling; Social Robots; Safe Reinforcement Learning; Shielding; Recurrent Neural Network; Gated Recurrent Unit; Människa-Robot Interaktion; Uppbackning; Sociala Robotar; Säker Förstärkningsinlärning; Avskärmning; Återkommande Neurala Nätverk; Gated Återkommande Enhet;

   Sammanfattning : Achieving appropriate and natural backchanneling behavior in social robots remains a challenge in Human-Robot Interaction (HRI). This thesis addresses this issue by utilizing methods from Safe Reinforcement Learning in particular shielding to improve social robot backchanneling behavior. LÄS MER

3. 2. Improving Behavior Trees that Use Reinforcement Learning with Control Barrier Functions : Modular, Learned, and Converging Control through Constraining a Learning Agent to Uphold Previously Achieved Sub Goals

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jannik Wagner; [2023]
   Nyckelord :Behavior Trees; Reinforcement Learning; Control Barrier Functions; Robotics; Artificial Intelligence; Verhaltensbäume; Verstärkendes Lernen; Kontrollbarrierefunktionen; Robotik; Künstliche Intelligenz; Beteendeträd; Förstärkningsinlärning; Kontrollbarriärfunktioner; Robotik; Artificiell Intelligens;

   Sammanfattning : This thesis investigates combining learning action nodes in behavior trees with control barrier functions based on the extended active constraint conditions of the nodes and whether the approach improves the performance, in terms of training time and policy quality, compared to a purely learning-based approach. Behavior trees combine several behaviors, called action nodes, into one behavior by switching between them based on the current state. LÄS MER

4. 3. Safe Reinforcement Learning for Human-Robot Collaboration : Shielding of a Robotic Local Planner in an Autonomous Warehouse Scenario

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Lukas Vordemann; [2022]
   Nyckelord :Human-Robot Collaboration; Safe Reinforcement Learning; Shielding; Risk Management; Autonomous Warehouse; Människa-Robot Samarbete; Säker Förstärkningsinlärning; Avskärmning; Riskhantering; Autonomt Lager;

   Sammanfattning : Reinforcement Learning (RL) is popular to solve complex tasks in robotics, but using it in scenarios where humans collaborate closely with robots can lead to hazardous situations. In an autonomous warehouse, mobile robotic units share the workspace with human workers which can lead to collisions, because the positions of humans or non-static obstacles are not known by the robot. LÄS MER

5. 4. Safe Reinforcement Learning for Remote Electrical Tilt Optimization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Grigorios Iakovidis; [2021]
   Nyckelord :Remote Electrical Tilt; Antenna Tilt Optimization; Reinforcement Learning; SafeReinforcement Learning; Fjärrlutning; Antennlutningsoptimering; Förstärkningsinlärning; Säker Förstärkningsinlärning;

   Sammanfattning : The adjustment of the vertical tilt angle of Base Station (BS) antennas, also known as Remote Electrical Tilt (RET) optimization, is a simple and efficient method of optimizing modern telecommunications networks. Reinforcement Learning (RL) is a machine learning framework that can solve complex problems like RET optimization due to its capability to learn from experience and adapt to dynamic environments. LÄS MER