Essays about: "Q- inlärning."

Showing result 1 - 5 of 29 essays containing the words Q- inlärning..

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

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : John Cao; Mikael Hammarling; [2023]
   Keywords : ;

   Abstract : 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. READ MORE

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

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Emilia Sjögren; Erika Weidenhaijn; [2023]
   Keywords : ;

   Abstract : 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. READ MORE

4. 3. S-MARL: An Algorithm for Single-To-Multi-Agent Reinforcement Learning : Case Study: Formula 1 Race Strategies

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Marinaro Davide; [2023]
   Keywords : Reinforcement Learning; Single-to-Multi-Agent; Learning Stability; Exploration-Exploitation trade-off; Race Strategy Optimization; Förstärkningsinlärning; Från en till flera agenter; Stabilitet vid inlärning; Utforskning-exploatering; Optimering av tävlingsstrategier;

   Abstract : A Multi-Agent System is a group of autonomous, intelligent, interacting agents sharing an environment that they observe through sensors, and upon which they act with actuators. The behaviors of these agents can be either defined upfront by programmers or learned by trial-and-error resorting to Reinforcement Learning. READ MORE

5. 4. An efficient deep reinforcement learning approach to the energy management for a parallel hybrid electric vehicle

   University essay from KTH/Skolan för industriell teknik och management (ITM)

   Author : Mingwei Liu; [2023]
   Keywords : HEV; EMS; Deep Reinforcement Learning; Learning Efficiency; Fuel Efficiency; HEV; EMS; Djup Förstärkningsinlärning; Inlärningseffektivitet; Bränsleeffektivitet;

   Abstract : In contemporary world, the global energy crisis and raise of greenhouse gas concentration in atmosphere necessitate the energy conservation and emission reduction. Hybrid electric vehicles (HEVs) can achieve great promise in reducing fuel consumption and greenhouse gas emissions by appropriate energy management strategies (EMSs). READ MORE

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

   University essay from KTH/Skolan för teknikvetenskap (SCI); KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Andreu Matoses Gimenez; [2023]
   Keywords : 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;

   Abstract : 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. READ MORE