Sökning: "Reward Function"

Visar resultat 21 - 25 av 85 uppsatser innehållade orden Reward Function.

1. 21. Improving a Reinforcement Learning Algorithm for Resource Scheduling

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

   Författare :Elin Wilson Andersson; Johan Håkansson; [2022]
   Nyckelord :Technology and Engineering;

   Sammanfattning : This thesis aims to further investigate the viability of using reinforcement learning, specifically Q-learning, to schedule shared resources on the Ericsson Many-Core Architecture (EMCA). This was first explored by Patrik Trulsson in his master thesis Dynamic Scheduling of Shared Resources using Reinforcement Learning (2021). LÄS MER

3. 22. Decision-support tool for identifying locations of shared mobility hubs : A case study in Amsterdam

   Master-uppsats, KTH/Transportplanering

   Författare :Pietro Podestà; [2022]
   Nyckelord :Shared-mobility hubs; GIS; cluster analysis; stated preference; discrete-choice models; hub usage prediction;

   Sammanfattning : Shared mobility is considered a more sustainable alternative to private modes. Nonetheless, its sudden and sometimes “out of control” emergence poses issues that need to be addressed. LÄS MER

4. 23. Benchmarking Deep Reinforcement Learning on Continuous Control Tasks : AComparison of Neural Network Architectures and Environment Designs

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

   Författare :Daniel Sahlin; [2022]
   Nyckelord :Deep learning; Reinforcement learning; Reward functions; Neural networks; Furuta pendulum; Djupinlärning; Förstärkningsinlärning; Belöningsfunktioner; Neurala nätverk; Furuta-pendel;

   Sammanfattning : Deep Reinforcement Learning (RL) has received much attention in recent years. This thesis investigates how reward functions, environment termination conditions, Neural Network (NN) architectures, and the type of the deep RL algorithm aect the performance for continuous control tasks. LÄS MER

5. 24. Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-Learning

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

   Författare :Cem Alptürk; [2022]
   Nyckelord :Technology and Engineering;

   Sammanfattning : We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. LÄS MER

7. 25. Explainable Reinforcement Learning for Risk Mitigation in Human-Robot Collaboration Scenarios

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

   Författare :Alessandro Iucci; [2021]
   Nyckelord :Explainable Reinforcement Learning; Human-Robot Collaboration; Risk Mitigation; Reward Decomposition; Autonomous Policy Explanation; Collaborative Robots; Förklarbar förstärkningslärande; Mänskligt-robot-samarbete; Riskreducering; Reward Decomposition; Autonomous Policy Explanation; Samarbetsrobotar;

   Sammanfattning : Reinforcement Learning (RL) algorithms are highly popular in the robotics field to solve complex problems, learn from dynamic environments and generate optimal outcomes. However, one of the main limitations of RL is the lack of model transparency. This includes the inability to provide explanations of why the output was generated. LÄS MER