Learning to explore using active neural SLAM

Advances in machine learning, computer vision and robotics have opened up avenues of building intelligent robots which can navigate in the physical world and perform complex tasks in our homes and offices. Exploration is a key challenge in building intelligent navigation agents. When an autonomous agent is dropped in an unseen environment, it needs to explore as much of the environment as fast as possible. How do we go about training autonomous exploration agents? One popular approach is using end-to-end deep Reinforcement Learning (RL).