使用变压器模型和激光雷达传感器基于动作识别的Flappy Bird游戏
Iveta Dirgová Luptáková1, Martin Kubovčík1, Jiří Pospíchal1
1Institute of Computer Technologies and Informatics, Faculty of Natural Sciences, University of Ss. Cyril and Methodius, J. Herdu 2, 917 01 Trnava, Slovakia.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究使用了变压器神经网络和LIDAR射线造用于Flappy Bird中的强化学习. 代理人成功地学会了避开障碍物,超过现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 弗拉皮鸟游戏是强化学习控制任务的流行基准.
- 传统的方法通常依赖于视觉输入 (游戏染图像).
- 需要替代的感官输入和方法来实现强大的代理导航.
研究的目的:
- 开发一种能够在Flappy Bird游戏中导航的强化学习代理.
- 为了研究使用LIDAR (光检测和测距) 传感输入的有效性,通过射线造处理,用于控制代理.
- 通过分析时间上下文和接近障碍物来加强避免障碍.
主要方法:
- 利用变压器神经网络架构来预测Q值.
- 使用强化学习技术来训练代理人.
- 实施了射线造方法来表示LIDAR感觉输入,专注于时间测量.
- 通过考虑接近障碍物的程度来优化代理行为的行为.
主要成果:
- 代理人成功地学会了在Flappy Bird环境中导航.
- 该剂有效地利用射线造测量,以避免与障碍物的碰撞.
- 拟议的模型显示了相对于相关方法的实质性性能改进.
结论:
- 变压器神经网络与LIDAR射线造相结合,为强化学习代理提供了有效的视觉输入替代方案.
- 这种方法通过利用时间感官数据来增强避障能力.
- 该方法在现实世界的导航和控制场景中显示出应用的希望.
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