Work

Multi-agent system with Policy Gradient Reinforcement Learning for RoboCup Soccer Simulator

Year: 2014

Type: dissertation

Abstract: The RoboCup Soccer Simulator is a multi-agent soccer simulator used in competitions to simulate soccer playing robots. These competitionsare mainly held to promote robotics and AI research by providing a cheap and accessible way to program robot-like agents. In this report alearning multi-agent soccer team is implemented, described and tested.Policy Gradient Reinforcement Learning (PGRL) is used to train and alter the strategical decision making of the agents. The results show that PGRL improves the performance of the learningteam. But when the gap in performance between the learning team and the opponent is big the results were inconclusive.

Source: KTH Publication Database DiVA (KTH Royal Institute of Technology)

Authors: Viktor Gavelli, Alexander Gómez

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W587903354

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