How AI Got Much Faster at Learning to Play Games by Sharing the Work
Researchers built a system where many computers work together so an AI can learn from games much quicker. Some machines keep playing and creating new moves, others study what happened, and all share a big pool of past play. This team showed the idea with a popular game-learning method and it learned way faster than the single-machine version. They tried it on 49 games and it did better in most of them, so that was a clear win.
The trick is simple, split the job and let parts help each other, so the AI sees more situations and improves faster. The system is distributed across many computers, it uses shared experience to train, and because of that the time to reach good play dropped by about t…
How AI Got Much Faster at Learning to Play Games by Sharing the Work
Researchers built a system where many computers work together so an AI can learn from games much quicker. Some machines keep playing and creating new moves, others study what happened, and all share a big pool of past play. This team showed the idea with a popular game-learning method and it learned way faster than the single-machine version. They tried it on 49 games and it did better in most of them, so that was a clear win.
The trick is simple, split the job and let parts help each other, so the AI sees more situations and improves faster. The system is distributed across many computers, it uses shared experience to train, and because of that the time to reach good play dropped by about ten times on many games. It shows how teamwork between machines can make smart programs learn quicker and play stronger, and this could help other AI tasks outside gaming too.
Read article comprehensive review in Paperium.net: Massively Parallel Methods for Deep Reinforcement Learning
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