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探索未见的东西:用行为信息增强语言条件的政策概括
Longhui Cao1,2, Chao Wang1,2, Juntong Qi1,2
1School of Future Technology, Shanghai University, Shanghai, China.
Cyborg and bionic systems (Washington, D.C.)
|January 29, 2024
概括
本研究引入了一种新的框架,即基于行为预测 (EMMA-BBP) 的多模式注意力的实体映射器,以改进强化学习概括. EMMA-BBP有效地解决了代理人的运动模两可,显著提高了他们适应新环境的能力.
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:
- 强化学习 (RL) 代理人努力将政策泛化到未见的环境中.
- 以语言为条件的政策利用语言信息进行跨环境的概括.
- 现有方法面临的挑战是,在具有相同名称但行为不同的实体中,运动模两可.
研究的目的:
- 为了应对语言条件强化学习中运动模两可的挑战.
- 增强RL代理在各种环境中的概括能力.
- 提出一个新的框架,有效地整合环境和文本信息.
主要方法:
- 开发了基于行为预测 (EMMA-BBP) 框架的多模式关注的实体映射器.
- 实现了一种行为预测模块,以辨别实体运动信息并解决语义模两可.
- 集成了一个文本匹配模块,以使环境文本与观察到的实体行为保持一致,过虚假信息.
主要成果:
- 在EMMA-BBP框架成功地消除对实体的动作信息的模糊性.
- 文本匹配模块有效地过出不相关或错误的文本信息.
- 与以前的方法相比,EMMA-BBP在MESSENGER环境中显示了一般化能力的翻倍.
结论:
- 拟议的EMMA-BBP框架显著改善了语言条件强化学习剂的泛化.
- 通过解决动作模两可的问题,EMMA-BBP提高了在复杂,跨环境任务中的剂量性能.
- 这种方法为开发更强大,更适应的人工智能代理提供了有希望的方向.
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