Ensemble UCT Needs High Exploitation
This work addresses performance improvements for large-scale parallelism of MCTS, but it is incremental as it builds on existing MCTS variations.
The paper tackled the problem of optimizing the exploitation-exploration balance in Ensemble UCT, a variation of MCTS, and found that increasing exploitation improves performance as search trees become smaller.
Recent results have shown that the MCTS algorithm (a new, adaptive, randomized optimization algorithm) is effective in a remarkably diverse set of applications in Artificial Intelligence, Operations Research, and High Energy Physics. MCTS can find good solutions without domain dependent heuristics, using the UCT formula to balance exploitation and exploration. It has been suggested that the optimum in the exploitation- exploration balance differs for different search tree sizes: small search trees needs more exploitation; large search trees need more exploration. Small search trees occur in variations of MCTS, such as parallel and ensemble approaches. This paper investigates the possibility of improving the performance of Ensemble UCT by increasing the level of exploitation. As the search trees becomes smaller we achieve an improved performance. The results are important for improving the performance of large scale parallelism of MCTS.