Deep Reinforcement Learning in Complex Environments
Where am I, and where am I going, and where have I been before? Answering these questions requires cognitive navigation skills--fundamental skills which are employed by every intelligent biological species to find food, evade predators, and return home. Mammalian species, in particular, solve navigation tasks through integration of several core cognitive abilities: spatial representation, memory, and planning and control. I will present current research which demonstrates how artificial agents can learn to solve navigation tasks through end-to-end deep reinforcement learning algorithms which are inspired by biological models. Further, I will show how these agents can learn to traverse entire cities by using Google Street View, without ever using a map.
Raia Hadsell, a senior research scientist at DeepMind, has worked on deep learning and robotics problems for over 10 years. Her thesis on Vision for Mobile Robots won the Best Dissertation award from New York University, and was followed by a post-doc at Carnegie Mellon's Robotics Institute. Raia then worked as a senior scientist and tech manager at SRI International. Raia joined DeepMind in 2014, where she leads a research team studying robot navigation and lifelong learning.