Reinforcement Learning Breakthrough: AI Designs Faster Ways to Multiply Matrices
Authors:
- Alhussein Fawzi
- Matej Balog
- Aja Huang
- Thomas Hubert
- Bernardino Romera-Paredes
- Mohammadamin Barekatain
- Alexander Novikov
- Francisco J. R. Ruiz
- Julian Schrittwieser
- Grzegorz Swirszcz
- David Silver
- Demis Hassabis
- Pushmeet Kohli
Abstract
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective...
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