在使用TextWorld的强化学习设置中分解用户定义的任务.
Thanos Petsanis1, Christoforos Keroglou1, Athanasios Ch Kapoutsis2
1School of Engineering, Department of Electrical and Computer Engineering, Democritus University of Thrace (DUTH), Xanthi, Greece.
Frontiers in robotics and AI
|January 8, 2024
概括
本研究引入了层次增强学习 (HRL),以简化自主代理的复杂任务. 这种方法通过分离行动并实现密集的奖励来增强代理人培训,从而提高整体绩效.
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:
- 复杂的任务给培训自主代理人带来了重大挑战.
- 在模拟环境中稀缺的奖励功能往往会阻碍有效的学习.
- 层次增强学习 (HRL) 为任务分解提供了一个潜在的解决方案.
研究的目的:
- 建议和评估一种新的等级强化学习 (HRL) 方法用于自主代理培训.
- 证明任务分解在提高代理学习效率方面的好处.
- 为了利用高层次的抽象来增强奖励功能设计.
主要方法:
- 使用TextWorld和MiniGrid Python环境实现一个层次化的强化学习 (HRL) 框架.
- 使用MiniGrid进行2D环境模拟和TextWorld进行高级任务抽象.
- 基于任务抽象,为低级环境设计密集的奖励函数.
- 采用正式方法来验证拟议算法的解决方案寻找能力.
主要成果:
- 拟议的HRL方法成功地将复杂的任务分解为可管理的子任务.
- 关于TextWorld抽象的培训使操纵和导航脱而出,简化了学习过程.
- 与稀疏奖励相比,使用密集奖励函数显著提高了代理人的训练表现.
- 正式方法证实,该算法能够导出解决方案.
结论:
- 具有任务抽象的等级增强学习 (HRL) 为培训自主代理提供了有效的策略.
- 文本世界和MiniGrid的整合促进了HRL的实际和有效实施.
- 解开行动和使用密集的奖励是提高模拟环境中代理业绩的关键因素.
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