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.

There's a lot in this post. The navigation accomplishment itself is impressive, and the discussion of the scaling properties of DD-PPO is also interesting. This was a topic area I hadn't delved into before.


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