Reinforcement learning based on multi-agent in robocup

Authors:
Wei Zhang;Jiangeng Li;Xiaogang Ruan
Affiliations:
School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, P.R. China;School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, P.R. China;School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, P.R. China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 5
Cited 0

Machine Learning

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Introduction to Multiagent Systems

Introduction to Multiagent Systems
The RoboCup Synthetic Agent Challenge 97

RoboCup-97: Robot Soccer World Cup I
Multiagent reinforcement learning using function approximation

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

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Abstract

Multi-agent systems form a particular type of distributed artificial intelligence systems. As an important character of players in game, autonomous agents' learning has become the main direction of researchers. In this paper, based on basic reinforcement learning, multi-agent reinforcement learning with specific context is proposed. The method is applied to RoboCup to learn coordination among agents. In the learning, the game field is divided into different areas, and the action choice is made dependent on the area in which the ball is currently located. This makes the state space and the action space decrease. After learning the optimal joint policy is determined. Comparison experiment between stochastic policy and this optimal policy shows the effectiveness of our approach.