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Facebook Speeds Up AI Training by Culling the Weak


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Facebook AI

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Facebook engineers have accelerated the training of artificial intelligence (AI) agents by eliminating laggards. The company created the Habitat series of photorealistic three-dimensional simulations to teach AI point-to-point navigation strategies—an essential capability for a practical "embodied AI" or robot.

Previous training systems wasted time waiting for slower agents to catch up, and the researchers created the Decentralized Distributed Proximal Policy Optimization (DD-PPO) system to eliminate these inefficient agents before they complete the task.

Whatever data is accumulated by these laggards is fed into the collective dataset. The DD-PPO system trained agents that navigated virtual environments from starting point to end goal with 99.9% reliability and fewer errors.

From TechCrunch 
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA


 

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