伪装和反向动力学建模用于数据高效的强化学习学习
IEEE transactions on neural networks and learning systems
|August 14, 2024
概括
蒙面和反向动力学建模 (MIND) 通过在变化状态下学习代理可控制的表示来提高深度强化学习中的数据效率. 这种自我监督的方法提高了在有限交互的控制环境中的性能.
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:
- 深度强化学习 (DRL) 在数据效率方面面临挑战,特别是在由于代理互动而演变的学习状态表示中.
- 现有的集成自主监督学习 (SSL) 和数据增强的方法很难明确地捕捉这些不断变化的状态动态或选择适当的增强.
研究的目的:
- 明确学习由代理行动和环境相互作用影响的变化状态的固有动态.
- 通过学习可靠和代理可控制的状态表示来提高基于像素的DRL中的数据效率.
主要方法:
- 建议掩盖和反向动力学建模 (MIND),一个使用变压器架构的自主监督多任务学习框架.
- 在MIND中,用于静态视觉表示的模具建模和用于演变状态表示的反向动态建模,采用掩饰增强.
- 该方法需要更少的超参数,并从连续中捕获时空信息.
主要成果:
- 在有限的相互作用下,MIND在离散和连续控制基准测试中表现出卓越的性能.
- 与以前的方法相比,这种方法显著提高了数据效率.
- 在动态环境中成功学习了代理可控制的表示.
结论:
- MIND通过结合掩面和反向动态建模,有效地学习不断演变的状态表示.
- 拟议的方法为复杂环境中的DRL提供了一种更有效和更稳定的数据处理方法.
- 该框架为推进DRL研究和应用提供了一个有希望的方向.
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