Navigating through real-world environments is a basic capability of intelligent agents. In "Learning to Navigate in Cities Without a Map", we present a deep RL architecture that captures locale-specific features while enabling transfer to multiple cities: https://t.co/I7rRY1Yxe8 pic.twitter.com/l7FESxgLVp

"Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here'') and a representation of the goal ("I am going there'').



Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale."