Uber Fiber is an Open Source Framework to Distribute Compute for Reinforcement Learning Models

The new framework simplifies distributed and scalable training for reinforcement learning agents.

Distributed computing methods are required across many areas of the machine learning lifecycle from training to simulations. In supervised learning methods, we already seem progress with distributed training frameworks like Horovod. However, DRL scenarios introduced their own set of challenges when comes to a distributed computing infrastructure.

Intuitively, we tend to think that a framework for distributed training of supervised learning models should work for DRL methods. However, reality is a bit different. Given that DRL methods are often trained using a large variety simulations, we need a distributed computing framework that adapts to that unique environments. For starters, most…

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