This question is regarding the Monte Carlo Tree Search (MCTS) algorithm presented in the AlphaZero paper (arXiv). As described in the paper, each MCTS used 800 simulations to determine the next action. This process builds a search subtree downwards from the root note. During this process, statistics about the nodes (e.g. values & visit counts) are updated in backward passes upwards through the tree. After all 800 simulations are complete, the most promising child node is selected (i.e. the node with the most visits, normalized by temperature), and then 800 new MCTS simulations are started using the selected child node as the new root node.
Question: Once the next round of 800 MCTS simulations starts, do we discard the statistics from the previous tree and thereby start with a "fresh" subtree, or do we keep the statistics gathered from the previous round of simulations?
I have found several tutorials/blog posts/repositories that implement either of these options and are contradictory. Furthermore, the wording in the paper seems ambiguous as they speak of "restarting" but it is not clear whether they restart after every round of 800 MCTS simulations or after each game is complete.