Google improves AI model training by open-sourcing framework

Google improves AI model training by open-sourcing framework
Editor at TechForge Media. Often sighted at global tech conferences with a coffee in one hand and laptop in the other. If it's geeky, I'm probably into it.

Google is helping researchers seeking to train AI models by open-sourcing a reinforcement learning framework used for its own projects.

Reinforcement learning has been used for some of the most impressive AI demonstrations thus far, including those which beat human professional gamers at Alpha Go and Dota 2. Google subsidiary DeepMind uses it for its Deep Q-Network (DQN).

Building a reinforcement learning framework takes both time and significant resources. For AI to reach its full potential, it needs to become more accessible.

Starting today, Google is making an open source reinforcement framework based on TensorFlow – its machine learning library – available on GitHub.

Pablo Samuel Castro and Marc G. Bellemare, Google Brain researchers, wrote in a blog post:

“Inspired by one of the main components in reward-motivated behavior in the brain and reflecting the strong historical connection between neuroscience and reinforcement learning research, this platform aims to enable the kind of speculative research that can drive radical discoveries.

This release also includes a set of collabs that clarify how to use our framework.”

Google’s framework was designed with three focuses: flexibility, stability, and reproducibility.

The company is providing 15 code examples for the Arcade Learning Environment — a platform which uses video games to evaluate the performance of AI technology — along with four distinct machine learning models: C51, the aforementioned DQN, Implicit Quantile Network, and the Rainbow agent.

Reinforcement learning is among the most effective methods of training. If you’re training a dog, offering treats as a reward for the desired behaviour is a key example of positive reinforcement in practice.

Training a machine is a similar concept, only the rewards are delivered or withheld as ones and zeros instead of tasty goods or a paycheck.

“Our hope is that our framework’s flexibility and ease-of-use will empower researchers to try out new ideas, both incremental and radical,” wrote Bellemare and Castro. “We are already actively using it for our research and finding it is giving us the flexibility to iterate quickly over many ideas.”

“We’re excited to see what the larger community can make of it.”

What are your thoughts on Google’s open-sourcing of its reinforcement learning framework? Let us know in the comments.

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