符号视觉增强学习:一个可扩展的框架,具有对象级抽象和可差异化的表达式搜索.
IEEE transactions on pattern analysis and machine intelligence
|September 26, 2024
概括
本研究介绍了差异化符号表达式搜索 (DiffSES),这是一种在复杂的视觉环境中创建可解释和高效的强化学习 (RL) 策略的新方法. DiffSES通过将符号推理与神经网络特征学习相结合,提高了可扩展性和性能.
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) 政策,特别是在视觉环境中,往往是黑子,阻碍了可解释性和高效部署.
- 现有的象征性RL (SRL) 框架在具有复杂对象交互的高维视觉场景中与特征学习和可扩展性作斗争.
研究的目的:
- 开发一种新的象征性学习方法,用于发现离散的象征性政策.
- 解决视觉RL中当前SRL方法的局限性,重点是可解释性,效率和可扩展性.
主要方法:
- 提出了微分符号表达式搜索 (DiffSES),一种使用部分微分优化来发现符号策略的方法.
- 使用对象级抽象而不是原始像素,将符号推理与基于神经网络的特征学习相结合.
主要成果:
- DiffSES生成比最先进的SRL方法更简单,更可扩展的象征性策略.
- 这种方法需要更少的象征性先前知识,同时保持竞争性表现.
结论:
- DiffSES为学习视觉RL中的高效和可解释的政策提供了一个有希望的方向.
- 该方法有效地平衡了符号方法和深度学习的优势,用于复杂的视觉任务.
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