Navigating with grid-like representations in artificial agents DeepMind

More broadly, our work reaffirms the potential of utilising algorithms thought to be used by the brain as inspiration for machine learning architectures. The extensive previous neuroscience research into grid cells makes the agent's interpretability - which is itself a major topic in AI research - significantly easier, by giving us clues about what to look for when trying to understand its internal representations. The work also showcases the potential of using artificial agents actively engaging in complex behaviours within realistic virtual environments to test theories of how the brain works. Taking this principle further, a similar approach could be used to test theories concerning brain areas that are important for perceiving sound or controlling limbs, for example. In the future such networks may well provide a new way for scientists to conduct'experiments', suggesting new theories and even complementing some of the work that is currently conducted in animals.