# Structure Learning in Motor Control:A Deep Reinforcement Learning Model

arXiv: https://arxiv.org/abs/1706.06827 · HTML: https://mlsanity.com/paper/1706.06827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes](https://mlsanity.com/paper/2109.12434/) (2021)
- [Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration](https://mlsanity.com/paper/2302.04250/) (2023)

## Abstract (arXiv)

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. We leverage this insight to address motor learning, by importing it into the setting of model-based reinforcement learning. We apply the resulting processing architecture to empirical findings from a landmark study of structure learning in target-directed reaching (Braun et al., 2009), and discuss its implications for a wider range of learning-to-learn phenomena.