Sökning: "Agenter"

Visar resultat 11 - 15 av 389 uppsatser innehållade ordet Agenter.

1. 11. Explainable AI for Multi-Agent Control Problem

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Hanna Prokopova; [2023]
   Nyckelord :;

   Sammanfattning : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. LÄS MER

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

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

   Författare :Marinaro Davide; [2023]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

4. 13. On the strategic abilities gained by using knowledge-based strategies for multi-agent teams playing against nature

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

   Författare :Sam Maltin; Simon Rosén; [2023]
   Nyckelord :;

   Sammanfattning : Analogous to how the Knowledge-Based Subset Construct (KBSC) can transform games of imperfect information against nature into games of perfect information, making it easier to find winning strategies for objectives in these games, the multi-agent knowledge-based subset construct (MKBSC) can be used to find winning strategies in multi-agent games of imperfect information. The difference between the two algorithms is that the generalized version can be applied multiple times, which is interpreted as the agent’s considering not just their own knowledge of the game, but also their knowledge of the other agents, and the knowledge of the other agent’s knowledge about their own knowledge, etc. LÄS MER

5. 14. PVCFA: Principal Variation Context Feature Attribution : Distributed Chess for Perturbation-based Saliency Maps

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

   Författare :Marco Molinari; [2023]
   Nyckelord :Chess; Perturbation; Saliency maps; Multiagent; Reinforcement Learning; Schack; Perturbation; Saliency maps; Multiagent; Reinforcement Learning;

   Sammanfattning : The research and development field of computer chess improved more in the last 5 years than in the whole history of computers. Unfortunately these unprecedented results comes with techniques that don’t leave much space to intuition and comprehensibility for humans. LÄS MER

7. 15. Simplifying multi-agent games with imperfect information against nature using predetermined strategies : Reducing the complexity of strategy synthesis for games by treating things in our control as if they were out of our control

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

   Författare :Oskar Malmström; [2023]
   Nyckelord :Multi-agent Games; Imperfect Information; Strategy Synthesis; Abstraction Construction; Fleragentsspel; Imperfekt Information; Strategisyntes; Abstraktionskonstruktion;

   Sammanfattning : We study games on graphs, where a coalition of agents work against an adversarial nature to achieve an objective. The agents have to collaborate while making their moves simultaneously, while receiving differing information about the state of the game and without a means of agent-to-agent communication. LÄS MER