Structure Learning in Motor Control:A Deep Reinforcement Learning Model

Understand

Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure.

• Although this `learning-to-learn' effect is well documented, its underlying computational mechanisms are poorly understood.
• We present a new model of motor structure learning, approaching it from the point of view of deep reinforcement learning.
• Previous work outside of motor control has shown how recurrent neural networks can account for learning-to-learn effects.