  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.
