Essays about: "deep multi-agent reinforcement learning"

Showing result 1 - 5 of 21 essays containing the words deep multi-agent reinforcement learning.

1. 1. LEO Satellite Connectivity for flying vehicles

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

   Author : Jinxuan Chen; [2023]
   Keywords : LEO satellite network; satellite connectivity strategy; Nash-SAC; flying vehicles; LEO:s satellitnät; Strategi för satellitanslutning; Nash-SAC; flygande fordon;

   Abstract : Compared with the terrestrial network (TN), which can only support limited covered areas, satellite communication (SC) can provide global coverage and high survivability in case of an emergency like an earthquake. Especially low-earth orbit (LEO) satellites, as a promising technology, which is integral to achieving the goal of global seamless coverage and reliable communication, catering to 6G’s communication requirements. READ MORE

3. 2. Reinforcement Learning for Multi-Agent Strategy Synthesis Using Higher-Order Knowledge

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

   Author : Gustav Forsell; Shamoun Gergi; [2023]
   Keywords : Higher Order Knowledge; Imperfect Information; Reinforcement Learning; Deep Q- networks; Knowledge Representation; Pursuit Evasion Games;

   Abstract : Imagine for a moment we are living in the distant future where autonomous robots are patrollingthe streets as police officers. Two such robots are chasing a robber through the city streets. Fearingthe thief might listen in to any potential transmission, both robots remain radio silent and are thuslimited to a strictly visual pursuit. READ MORE

4. 3. 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

5. 4. Enhancing video game experience with playtime training and tailoring of virtual opponents : Using Deep Q-Network based Reinforcement Learning on a Multi-Agent Environment

   University essay from

   Author : Nishant Pillai; Roberto Giaconia; [2023]
   Keywords : ;

   Abstract : When interacting with fictional environments, the users' sense of immersion can be broken when characters act in mechanical and predictable ways. The vast majority of AIs for such fictional characters, that control their actions, are statically scripted, and expert players can learn strategies that take advantage of this to easily win challenges that were intended to be hard. READ MORE

6. 5. Uncontrolled intersection coordination of the autonomous vehicle based on multi-agent reinforcement learning.

   University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Author : Isaac Arnold McSey; [2023]
   Keywords : Autonomous Vehicles AVs ; Road Safety; Fuel Efficiency; Business Dynamics; Intersections; Human-Driven Vehicles HDVs ; Pedestrians; Multi-Agent Reinforcement Learning MARL ; Multi-Agent Deep Deterministic Policy Gradient MADDPG ; Algorithmic Interactions; Uncontrolled Intersections; Global Insights; Safety Improvements; Comfort Improvements; Learning Process; Global Experiences; Complex Environments; Passenger Comfort; Navigation;

   Abstract : This study explores the application of multi-agent reinforcement learning (MARL) to enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs)at uncontrolled intersections. The research aims to assess the potential of MARL in modeling multiple agents interacting within a shared environment, reflecting real-world situations where AVs interact with multiple actors. READ MORE