促使决策转换器为零射击范围避免政策.
1Massachusetts Institute of Technology, Harvard Medical School.
ArXiv
|June 10, 2025
概括
我们介绍了RADT,这是一种新的机器人学习人工智能模型,可以有效地学习达到目标,同时避免危险区域. 拉特在复杂的场景中表现出色,并且在不需要再培训的情况下适应新的避开任务.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 强化学习是一种强化学习.
背景情况:
- 离线目标条件强化学习 (RL) 对于达到避免任务是有效的.
- 当前的方法与动态避开区域规范作斗争,并需要精心设计的奖励功能.
- 这限制了复杂环境中的可扩展性.
研究的目的:
- 介绍RADT,一个决策转换器,用于离线,无奖励,目标条件和避免区域条件的RL.
- 在评估时实现多个避免区域的灵活,动态规范.
- 在没有明确奖励的情况下,从低于最佳的数据中学习有效的覆盖范围 - 避免政策.
主要方法:
- 在决策转换器架构中,RADT编码目标并避免区域作为提示令牌.
- 使用了目标和避免区域事后重新标记的新组合.
- 从随机政策中训练在非最佳离线轨迹上.
主要成果:
- RADT展示了零射击的概括,以避免分布外的区域大小和数量.
- 在11个不同的任务中超越现有的离线目标条件的RL模型.
- 在保持高成功率的同时,实现了正常化成本 (35.7%) 的显著改善.
结论:
- 在复杂的,结构不良的环境中,RADT提供了一种灵活和可扩展的方法来实现避免任务.
- 它处理动态避开区域和无奖励学习的能力是一个显著的进步.
- 成功应用于生物细胞重编程,减少不必要的状态访问.
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