从单个专家数据集学习规定的安全性能模拟学习
概括
拉格朗的生成对抗模仿学习 (LGAIL) 能够在各种约束下进行自适应的安全政策学习. 这种方法动态地平衡模仿和安全,优于现有的安全模仿学习方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现有的安全模仿学习 (safe IL) 方法主要集中在复制专家政策上,这些政策可能无法满足不同的或新的安全约束.
- 这种限制阻碍了在需要适应性安全规范的场景中应用安全IL.
研究的目的:
- 提出一种新的算法,拉格朗的生成对抗模拟学习 (LGAIL),能够在各种安全约束下从专家数据中自适应学习安全策略.
- 解决当前安全IL方法在处理各种安全要求方面的局限性.
主要方法:
- 增加具有安全约束的生成对抗模拟学习 (GAIL) 并使用拉格朗奇乘数将其放松为不受约束的问题.
- 采用两阶段的优化:用于数据相似性的区分器训练和用于模仿和安全的前向增强学习,由动态调整的拉格朗日乘数指导.
主要成果:
- 在规定的约束条件下,理论分析证明了LGAIL的融合和安全保证.
- 在OpenAI安全健身房进行了广泛的实验,验证了拟议方法的有效性.
结论:
- LGAIL有效地学习适应性安全政策,满足各种安全约束,优于现有的安全IL方法.
- 拉格朗奇乘数的动态调整对于平衡模仿性能和安全遵守至关重要.
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