High-level representations through unconstrained sensorimotor learning

How the sensorimotor experience of an agent can be organized into abstract symbol-like structures to enable effective planning and control is an open question. In the literature, there are many studies that start by assuming the existence of some symbols and ‘ground’ those onto continuous sensorimotor signals. There are also works that aim to facilitate the emergence of symbol-like representations by using specially designed machine learning architectures. In this paper, we investigate whether a deep reinforcement learning system that learns a dynamic task would facilitate the formation of high-level neural representations that might be considered as precursors of symbolic representation, which could be exploited by higher level neural circuits for better control and planning. The results indicate that without even explicit design to promote such representations, neural responses emerge that may serve as the basis of abstract symbol-like representations.