  AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree
Search (MCTS), has successfully trained reinforcement learning agents in a
tabula-rasa way. The neural MCTS algorithm has been successful in finding
near-optimal strategies for games through self-play. However, the AlphaZero
algorithm has a significant drawback; it takes a long time to converge and
requires high computational power due to complex neural networks for solving
games like Chess, Go, Shogi, etc. Owing to this, it is very difficult to pursue
neural MCTS research without cutting-edge hardware, which is a roadblock for
many aspiring neural MCTS researchers. In this paper, we propose a new neural
MCTS algorithm, called Dual MCTS, which helps overcome these drawbacks. Dual
MCTS uses two different search trees, a single deep neural network, and a new
update technique for the search trees using a combination of the PUCB, a
sliding-window, and the epsilon-greedy algorithm. This technique is applicable
to any MCTS based algorithm to reduce the number of updates to the tree. We
show that Dual MCTS performs better than one of the most widely used neural
MCTS algorithms, AlphaZero, for various symmetric and asymmetric games.
