Abstract
Real-Time Strategy(RTS) games provide a challenging platform to implement online reinforcement learning(RL) techniques in a real application. Computer as one player monitors opponents'(human or other computers) strategies and then updates its own policy using RL methods. In this paper, we propose a multi-layer framework for implementing the online RL in a RTS game. The framework significantly reduces the RL computational complexity by decomposing the state space in a hierarchical manner. We implement the RTS game - Tank General, and perform a thorough test on the proposed framework. The results show the effectiveness of our proposed framework and shed light on relevant issues on using the RL in RTS games.
Originalsprog | Engelsk |
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Titel | 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology : Proceedings of 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 09) |
Antal sider | 4 |
Forlag | IEEE Computer Society Press |
Publikationsdato | 2009 |
Udgave | 2 |
Sider | 497-500 |
ISBN (Trykt) | 978-0-7695-3801-3 |
Status | Udgivet - 2009 |
Begivenhed | Conference on Intelligent Agent Technology - Milan, Italien Varighed: 15 sep. 2009 → 18 sep. 2009 |
Konference
Konference | Conference on Intelligent Agent Technology |
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Land/Område | Italien |
By | Milan |
Periode | 15/09/2009 → 18/09/2009 |