I didn’t say anything about chess or shogi because I don’t recall any ablation for A0, I just remember the one in the AG0 paper for Go. The AG0 is definitely at or close to professional level and better than ‘good amateur’. And I would consider a non-distributed PUCT with no rollouts or other refinements to be a ‘simple tree search’: it doesn’t do any rollouts, and the depth is seriously limited by running on only a single machine w/4 TPUs with a few seconds for search: as the AG0 paper puts it, “Finally, it uses a simpler tree search that relies upon this single neural network to evaluate positions and sample moves, without performing any Monte-Carlo rollouts...we chose to use the simplest possible search algorithm”.
I didn’t say anything about chess or shogi because I don’t recall any ablation for A0, I just remember the one in the AG0 paper for Go. The AG0 is definitely at or close to professional level and better than ‘good amateur’. And I would consider a non-distributed PUCT with no rollouts or other refinements to be a ‘simple tree search’: it doesn’t do any rollouts, and the depth is seriously limited by running on only a single machine w/4 TPUs with a few seconds for search: as the AG0 paper puts it, “Finally, it uses a simpler tree search that relies upon this single neural network to evaluate positions and sample moves, without performing any Monte-Carlo rollouts...we chose to use the simplest possible search algorithm”.