An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Motivated reinforcement learning for non-player characters in persistent computer game worlds
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
International Journal of Web Based Communities
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Levels of realism for cooperative multi-agent reinforcement learning
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Non-reciprocating Sharing Methods in Cooperative Q-Learning Environments
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
A virtual world is an online community in the form of a computer-based simulated environment, through which users can interact with one another and use and create objects The non-player characters (NPC) in virtual world are following a fixed set of pre-programmed behaviors and lack the ability to adapt with the changing surrounding Reinforcement learning agent is a way to deal with this problem However, in a cooperative social environment, NPC should learn not only by trial and error, but also through cooperation by sharing information The key investigation of this paper is: modeling the NPCs as multi-agent, and enable them to conduct cooperative learning, then speeding up the learning process By using a fire fighting scenario in Robocup Rescue, our research shows that sharing information between cooperative agents will outperform independent agents who do not communicate during learning The further work and some important issues of multi-agent reinforcement learning in virtual world will also be discussed in this paper.