Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning

@inproceedings{Tastan2011LearningPF,
  title={Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning},
  author={Bulent Tastan and Gita Reese Sukthankar},
  booktitle={AIIDE},
  year={2011}
}
The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which… CONTINUE READING
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