Sökning: "förstärkande inlärning"

Visar resultat 1 - 5 av 35 uppsatser innehållade orden förstärkande inlärning.

1. 1. Multi-Agent Deep Reinforcement Learning in Warehouse Environments

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

   Författare :John Cao; Mikael Hammarling; [2023]
   Nyckelord :;

   Sammanfattning : This report presents a deep reinforcement algorithm for multi-agent systems based on the classicalDeep Q-Learning algorithm. The method considers a decentralized approach to controlling theagents, by equipping each agent with its own neural network and replay memory. LÄS MER

3. 2. Deep Reinforcement Learning in Games Based on Extracted Features

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

   Författare :Emilia Sjögren; Erika Weidenhaijn; [2023]
   Nyckelord :;

   Sammanfattning : FlappyBird is a popular mobile game that captured many people's attention because itwas easy to understand but difficult to perform --- players were often right on the edge ofsucceeding, which led to a strong desire to play again. The purpose of this project is to investigatethe possibility of using a neural network trained with reinforcement learning to play the game usingextracted features rather than raw images. LÄS MER

4. 3. A Bandit Approach to Indirect Inference

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

   Författare :Erik Ildring; Felix Steinberger Eriksson; [2023]
   Nyckelord :;

   Sammanfattning : We present a novel approach to the family of parameter estimation methods known asindirect inference (II), using results from bandit optimization, a sub-field of reinforcementlearning concerned with stateless Markov decision processes (MDPs). First, we present theproblem of indirect inference and show how it may be cast into the general framework ofMDPs. LÄS MER

5. 4. Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps

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

   Författare :Victor Sanchez; [2023]
   Nyckelord :Deep Reinforcement Learning; Environment-aware navigation; Robotics; Artificial Intelligence; Apprentissage par renforcement profond; Navigation consciente de l’humain; Intelligence Artificielle; Robotique; Djup Förstärkande Inlärning; Människomedveten navigering; Robotik; Artificiell Intelligens;

   Sammanfattning : Reinforcement learning (RL) has seen a fast expansion in recent years of its successful application to a range of decision-making and complex control tasks. Moreover, deep learning offers RL the opportunity to enlarge its spectrum of complex fields. LÄS MER

7. 5. 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