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The rules of Go are not exactly "programmed in" to AlphaGo; they are only provided so the model knows what the valid moves and win conditions are as it self-tra
by gary_0 3y ago
The rules of Go are not exactly "programmed in" to AlphaGo; they are only provided so the model knows what the valid moves and win conditions are as it self-trains. The latest versions of the model can be quickly adapted to play other games[0] and MuZero can "master games without knowing their rules"[1]. Also, some of DeepMind's earliest work was a deep reinforcement learning model that could play seven different Atari games with no adjustments needed to switch games[2].
[0] https://en.wikipedia.org/wiki/AlphaGo_Zero https://en.wikipedia.org/wiki/AlphaGo_Zero
[1] https://en.wikipedia.org/wiki/MuZero https://en.wikipedia.org/wiki/MuZero
[2] https://arxiv.org/abs/1312.5602 https://arxiv.org/abs/1312.5602
- modeless 3y agoThe rules of Go very much are programmed in to AlphaGo. The neural nets are generic, sure. But the game rules programmed manually into the training system are vital to the system working. Even with MuZero: while it uses a world model which is learned, ultimately during training there is still grounding in the manually programmed rules of whatever game it's playing. When it trains on Atari, the Atari system (manually programmed, obviously) provides the grounding rules. What I am saying is there is no analogous source of manually programmed rules to ground language models during training.