Evolving AI: Genetic Algorithms Train Deep Neural Networks Fast
Imagine teaching a computer to learn by letting many versions compete and slowly improve.
Researchers found that a simple genetic algorithm can shape large, smart networks just as well as standard methods.
These networks, called deep neural networks, were evolved without using gradients — that means a no-gradient approach — and still solved hard play and movement tasks.
The surprise is how well it worked and how quick it can be; training that used to take long, now finishes much faster on regular machines.
Mixing evolution with a trick named novelty search helps when rewards are rare or misleading, because it rewards new behaviors not just high scores.
This opens up new, simpler ways to build smart systems, and makes lots of evolution tools ready to use.
It also suggests that following the usual path isn't always best, and sometimes letting solutions evolve can find unexpected wins.
Try imagine what new apps could come from this, it's exciting and practical, and definitely worth watching.
Read article comprehensive review in Paperium.net:
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative forTraining Deep Neural Networks for Reinforcement Learning
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