Robot-assisted motor training: Assistance decreases exploration during reinforcement learning

Abstract

Reinforcement learning (RL) is a form of motor learning that robotic therapy devices could potentially manipulate to promote neurorehabilitation. We developed a system that requires trainees to use RL to learn a predefined target movement. The system provides higher rewards for movements that are more similar to the target movement. We also developed a novel algorithm that rewards trainees of different abilities with comparable reward sizes. This algorithm measures a trainee's performance relative to their best performance, rather than relative to an absolute target performance, to determine reward. We hypothesized this algorithm would permit subjects who cannot normally achieve high reward levels to do so while still learning. In an experiment with 21 unimpaired human subjects, we found that all subjects quickly learned to make a first target movement with and without the reward equalization. However, artificially increasing reward decreased the subjects' tendency to engage in exploration and therefore slowed learning, particularly when we changed the target movement. An anti-slacking watchdog algorithm further slowed learning. These results suggest that robotic algorithms that assist trainees in achieving rewards or in preventing slacking might, over time, discourage the exploration needed for reinforcement learning.