CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning
Marvin Chancán,Michael Milford,Marvin Chancán,Michael Milford
Visual navigation tasks in real-world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real ro...
