Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

Abstract

Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.

A Control Interface for Autonomous Positioning of Magnetically Actuated Spheres Using an Artificial Neural Network

“…We found that startup and camera connection initiated long delays, with each operation taking ≈1 s. Beyond those two functions, all other functions were completed in significantly less than 100 ms. Future work demonstrating operability in an array of light sources, and possibly under actively changing lighting conditions, may shed light on how the anticipation of dynamic lighting conditions could be incorporated into the training set for untethered magnetic manipulation systems. Various laboratories have applied advanced learning techniques such as reinforcement learning to the problem of controlling small magnetic devices [48][49][50], and we acknowledge that the application of such learning approaches would improve the performance of our controller. However, here, we emphasize the ability to control a simple magnetic sphere using very simple regression models and a small sample data set.…”