It is the main idea of the algorithm, but as the paper itself says, there has been a trend towards this recently. I haven't read many of the papers they cite, b

It is the main idea of the algorithm, but as the paper itself says, there has been a trend towards this recently. I haven't read many of the papers they cite, but I do remember one of them ("Learning to predict without looking ahead: world models without forward prediction"). They had a neat site (learningtopredict.github.io). They write: "We speculate that the complexity of world models could be greatly decreased if they could fully leverage this idea: that a complete model of the world is actually unnecessary for most tasks - that by identifying the important part of the world, policies could be trained significantly more quickly, or more sample efficiently". That is just what this paper does, as I understand it, by bringing it together with the tree search from AlphaZero.