用相互信息来解决对抗模仿学习中的隐含偏见.
Lihua Zhang1, Quan Liu2, Fei Zhu2
1School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China.
概括
相互信息生成对抗模仿学习 (MI-GAIL) 纠正对抗模仿学习 (AIL) 奖励函数中的偏差. 这种方法提高了自动化决策系统的样本效率和培训稳定性.
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:
- 敌对模仿学习 (AIL) 通过模仿专家演示来训练自动决策系统.
- AIL算法在其奖励函数中存在隐性偏差,导致样本效率低下.
研究的目的:
- 为了解决由AIL中的奖励函数偏差引起的样本低效率问题.
- 建议相互信息生成对抗模仿学习 (MI-GAIL) 进行无偏的奖励函数设计.
主要方法:
- 开发了两个指导方针,用于设计无偏的奖励函数.
- 通过将基于潜在的奖励函数的辅助信息纳入,塑造了歧视者的奖励函数.
- 利用相互信息原则来指导奖励塑造.
主要成果:
- 与最先进的模仿学习算法相比,MI-GAIL显示了更好的样本效率.
- 拟议的方法提高了在持续控制任务中的培训稳定性.
- 实验结果验证了MI-GAIL在缓解AIL奖励偏差方面的有效性.
结论:
- MI-GAIL有效地纠正了AIL奖励函数中的隐性偏差.
- 拟议的方法为改善模仿学习系统的性能提供了一个有希望的方向.
- 这项工作为开发更样本高效和更稳定的自动化决策系统提供了基础.
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