C2RL:用于强化学习的卷积对比学习 基于自我预训的强化学习,用于强化学习的强化学习
Sanghoon Park1, Jihun Kim1, Han-You Jeong2
1Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种对比式学习方法,以提高强化学习的概括性. 该方法有效地使用强大的数据增强,而不妨碍性能,从而在未见的环境中更好地适应.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 强化学习 (RL) 代理人在未见的环境中与一般化作斗争,特别是与高维图像输入.
- 自主监督学习 (SSL) 与数据增强有助于RL概括,但可能会因过度的图像更改而被破坏.
研究的目的:
- 提出一种新的对比学习框架,以提高强化学习的概括性.
- 为了有效地管理RL性能和数据增强强度之间的权衡,以改善泛化.
主要方法:
- 开发了整合到RL架构中的对比学习方法.
- 在对比学习框架内利用数据增强策略来最大限度地提高辅助学习效应.
- 研究了不同数据增强强度对RL性能和概括性的影响.
主要成果:
- 提出的对比学习方法成功地利用了强大的数据增强.
- 与DeepMind Control套件上的现有方法相比,展示了优越的概括能力.
- 展示了强大的增强,当通过对比学习管理时,增强而不是破坏RL.
结论:
- 对比式学习提供了一个有效的解决方案,用于RL泛化问题与图像输入.
- 拟议的方法允许使用强大的数据增强来提高概括性,而不会影响RL任务性能.
- 这一框架在多样化和未见的测试场景中提高了强化学习代理的稳定性.
相关概念视频
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