Tank War Using Online Reinforcement Learning
Kresten Toftgaard Andersen, Yifeng Zeng, Dennis Dahl Christensen, Dung Tran · 2009
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.