Sökning: "Safe Reinforcement Learning"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden Safe Reinforcement Learning.

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. Scalable Reinforcement Learning for Formation Control with Collision Avoidance : Localized policy gradient algorithm with continuous state and action space

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

   Författare :Andreu Matoses Gimenez; [2023]
   Nyckelord :Control theory; Multi-agent systems; Distributed systems; Formation control; Collision avoidance; Reinforcement learning; Teoria de control; Sistemes multiagent; Sistemes distribuïts; Control de formació; Prevenció de col·lisions; Reinforcement Learning; Reglerteknik; Multi-agent system; Distribuerade system; formationskontroll; Kollisionsundvikande; Reinforcement learning; Teoría de control; Sistemas multiagente; Sistemas distribuidos; Control de formación; Prevención de colisiones; Reinforcement Learning;

   Sammanfattning : In the last decades, significant theoretical advances have been made on the field of distributed mulit-agent control theory. One of the most common systems that can be modelled as multi-agent systems are the so called formation control problems, in which a network of mobile agents is controlled to move towards a desired final formation. LÄS MER

4. 3. 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

5. 4. Edge Case searching for Autonomous Vehicles

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :Måns Sandsjö; Oscar Sundbom; [2022]
   Nyckelord :Technology and Engineering;

   Sammanfattning : In this thesis, a method that generates critical situations for autonomous vehicles with fewer resources was investigated. To produce these key examples, the strategy in this thesis explores optimization and reinforcement learning. These key examples are defined as Edge cases; these cases are situations on the border between safe and unsafe. LÄS MER

7. 5. Interaction Aware Decision Making for Automated Vehicles Based on Reinforcement Learning

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

   Författare :Ning Wang; [2022]
   Nyckelord :Reinforcement Learning; Automated Vehicles; Decision-making; Rollout; Driver Behaviour Modeling; Trajectory Prediction; Förstärkningsinlärning; automatiserade fordon; beslutsfattande; utrullning; modellering av förarbeteende; banförutsägelse;

   Sammanfattning : Decision-making is one of the key challenges blocking full autonomy of automated vehicles. In highway scenarios, automated vehicles are expected to be aware of their surroundings and make decisions by interacting with other road participants to drive safely and efficiently. LÄS MER