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.

强化学习是在模拟环境中进行的，以学习对多个电机轴的连续速度控制。然后将其应用于现实世界的光镊实验，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发生碰撞。在虚拟环境中训练神经网络的概念在机器学习用于实验优化和控制的应用中具有巨大的潜力，因为神经网络可以发现解决问题的最佳方法而不会损坏设备的风险，并且速度不受物理环境中运动的限制。由于神经网络同等对待虚拟环境和物理环境，因此我们表明该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。这项技术可能有潜力解锁与混合现实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其他传感器输入观察结果的方法。