> It doesn't seem possible to give "no explicit rules", unless you count "making an illegal move equivalent to a loss" as giving no explicit rules. Which doesn'

> It doesn't seem possible to give "no explicit rules", unless you count "making an illegal move equivalent to a loss" as giving no explicit rules. Which doesn't seem like anything but word laundering. The model is learning from game trajectories offline. Illegal moves will be assigned a very low probability because they do not appear in real games (whoever may be playing those games, whether in a simulation or in the real world). AlphaGo/AlphaZero did in fact mask out illegal moves in the MCTS tree search, but MuZero does not: > MuZero only masks legal actions at the root of the search tree where the environment can be queried, but does not perform any masking within the search tree.This is possible because the network rapidly learns not to predict actions that never occur in the trajectories it is trained on. And in ALE, all inputs are always legal, it's just that they may be useless and a waste of a 100ms turn. So for ALE it doesn't matter. Now, for Go/chess/shogi, they generate the training data for the supervised learning part by reusing MuZero for MCTS self-play. They don't mention how legal moves are handled there; they might be masking out like in AlphaZero. You could argue that this is, in some indirect way, 'explicit rules'. But since the MuZero is already learning to predict moves' value and which moves actually get taken, I see no reason that the MCTS self-play couldn't implement an instant-loss rule without any problem or slowing down training all that much, removing even that objection.