Near-perfect point-goal navigation from 2.5 billion frames of experience

The AI community has a long-term goal of building intelligent machines that interact effectively with the physical world, and a key challenge is teaching these systems to navigate through complex, unfamiliar real-world environments to reach a specified destination -- without a preprovided map. We are announcing today that Facebook AI has created a new large-scale distributed reinforcement learning (RL) algorithm called DD-PPO, which has effectively solved the task of point-goal navigation using only an RGB-D camera, GPS, and compass data. Agents trained with DD-PPO (which stands for decentralized distributed proximal policy optimization) achieve nearly 100 percent success in a variety of virtual environments, such as houses and office buildings. We have also successfully tested our model with tasks in real-world physical settings using a LoCoBot and Facebook AI's PyRobot platform. An unfortunate fact about maps is that they become outdated the moment they are created.