使用深度强化学习玩光学镊子：在虚拟，物理和增强环境中
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments
摘要：
在模拟环境中进行强化学习，以学习多个电机轴上的连续速度控制。然后将其应用于现实世界的光镊实验，其目的是将激光捕获的微球移动到目标位置，同时避免与其他自由移动的微球碰撞。在虚拟环境中训练神经网络的概念在将机器学习应用于实验优化和控制方面具有巨大的潜力，因为神经网络可以发现解决问题的最佳方法，而不会有损坏设备的风险，并且速度不受物理环境中移动的限制。由于神经网络对待虚拟和物理环境等效，我们表明，网络也可以应用于增强环境，其中虚拟环境与物理环境相结合。这种技术可能有潜力解锁与混合和增强现实相关的功能，例如强制执行机器运动的安全限制或作为从附加传感器输入观察结果的方法。
英文摘要：
Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
