Improvement of Agent Learning for a Card Game Based on Multi-channel ART Networks

@article{Nimoto2016ImprovementOA,
  title={Improvement of Agent Learning for a Card Game Based on Multi-channel ART Networks},
  author={Kenta Nimoto and Kenichi Takahashi and Michimasa Inaba},
  journal={JCP},
  year={2016},
  volume={11},
  pages={341-352}
}
The 3-channel fuzzy adaptive resonance theory network FALCON (Fusion Architecture for Learning, COgnition, and Navigation) is recognized as an effective method for combining reinforcement learning with state segmentation, in which learning targets the relationships between percepts, actions, and rewards. It has been shown that FALCON is effective in making a playing agent for the card game Hearts, although the agent was unable to beat a rule-based agent. This study proposes new learning methods… CONTINUE READING