Centre for Computational Statistics and Machine Learning

Navigation is an important cognitive task that enables humans and animals to traverse, with or without maps, over long distances in the complex world. Such long-range navigation can simultaneously support self-localisation (“I am here”) and a representation of the goal (“I am going there”). For this reason, studying navigation is fundamental to the study and development of artificial intelligence, and trying to replicate navigation in artificial agents can also help neuroscientists understand its biological underpinnings.
This talk will cover our own journey to understand navigation by building deep reinforcement learning agents, starting from learning to control a simple agent that can explore and memorise large 3D mazes, to building agents that can learn to read and write to memory in order to generalise goal acquisition skills to previously unseen environments. I will show how these artificial agents relate to navigation in the real world, both through the study of the emergence of grid cell representations in neural networks -- akin to those found in the mammalian entorhinal cortex -- and by demonstrating that these agents can navigate in Street View-based real world photographic environments.