Sfoglia per Correlatore  

Opzioni
Vai a: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mostrati risultati da 1 a 9 di 9
Tipologia Anno Titolo Titolo inglese Autore File
Lauree magistrali 2021 A NMPC based adversarial drivers for driving simulators A NMPC based adversarial drivers for driving simulators BRONCA, NICCOLÒ
Lauree magistrali 2011 Analysis of clocks synchronization algorithms in wireless sensor networks - D'Elia, Edoardo
Lauree triennali 2025 Automating Electrical Oversubscription Calculation in Data Centers Using AI Agents Automating Electrical Oversubscription Calculation in Data Centers Using AI Agents MICHELETTI, LORENZO
Lauree magistrali 2025 Autonomous Robot Action Sequence Learning with Teleoperation, Deep Reinforcement Learning, and LLM-Based Natural Language Input Autonomous Robot Action Sequence Learning with Teleoperation, Deep Reinforcement Learning, and LLM-Based Natural Language Input CHEKKALA, JAYANTHVIKRAM
Lauree magistrali 2021 Kalman filtering for temperature estimation of electric motors Kalman filtering for temperature estimation of electric motors SÁNCHEZ EL RYFAIE, SAMIRA CAROLINA
Lauree magistrali 2024 Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks Learning Simultaneously Policies and Action Sequences for Robotic Manipulation Tasks In this research, aim is to explore how robots can learn to perform complex tasks more effectively by combining reinforcement learning, behavior trees, and genetic programming. The idea is to help robots simultaneously figure out not just what actions to take, but also the best sequence of those actions to complete tasks like grasping or assembling objects. By using reinforcement learning, the robot can learn from trial and error, improving its decision-making over time. Behavior trees offer a structured way to define and adapt complex behaviors, making the robot's actions more flexible. Meanwhile, genetic programming will be used to evolve and optimize these behaviors, helping the robot find the most efficient strategies even in unpredictable environments. Ultimately, this research aims to create robots that are not only more capable but also more adaptable to the challenges they encounter in the real world. KURTOGLU, METEHAN
Lauree magistrali 2023 Learning stack of tasks for robotic mobile manipulation Learning stack of tasks for robotic mobile manipulation ADAMI, ALESSANDRO
Lauree magistrali 2025 Reinforcement Learning for Active Network Management in Micro Smart Grids Reinforcement Learning for Active Network Management in Micro Smart Grids PASE, EMANUELE
Lauree magistrali 2024 Speeding up Reinforcement Learning Algorithms Through Demonstrations: Application to Robotic Manipulation Speeding up Reinforcement Learning Algorithms Through Demonstrations: Application to Robotic Manipulation PANIZZO, MARCO
Mostrati risultati da 1 a 9 di 9
Legenda icone

  •  file ad accesso aperto
  •  file ad accesso riservato
  •  file sotto embargo
  •  nessun file disponibile