Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

AI Learns Poker by Playing Itself — New Reinforcement Learning Breakthrough

Imagine an AI that teach itself to play tricky games by playing again and again, with no human rules whispered in its ear.
The idea is clever: let it compete against itself, remember winning moves, forget bad ones, and slowly improve — this is called self-play.
Behind the scenes the method uses reinforcement learning, which rewards good choices so the computer tend to pick them later.
Tested on real card tables like poker, the system learnt plans that came close to top programs that were built with lots of expert tricks.
It didnt need an expert to point out the important moves, it figure much out by trial, error and repetition.
Not every training run ends perfect, sometimes it wander, sometimes surprises, but often it finds clever ways humans did not expect.
This change lets computers tackle uncertain, messy problems without hand-made rules.
If you like stories about smart tech that grows smarter by playing, watch this space — it's only the beginning of more flexible, self-taught AI.

Read article comprehensive review in Paperium.net:
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

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