  Since AlphaGo and AlphaGo Zero have achieved breakground successes in the
game of Go, the programs have been generalized to solve other tasks.
Subsequently, AlphaZero was developed to play Go, Chess and Shogi. In the
literature, the algorithms are explained well. However, AlphaZero contains many
parameters, and for neither AlphaGo, AlphaGo Zero nor AlphaZero, there is
sufficient discussion about how to set parameter values in these algorithms.
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how
these parameters contribute to training. We focus on three objectives~(training
loss, time cost and playing strength). For each parameter, we train 3 models
using 3 different values~(minimum value, default value, maximum value). We use
the game of play 6$\times$6 Othello, on the AlphaZeroGeneral open source
re-implementation of AlphaZero. Overall, experimental results show that
different values can lead to different training results, proving the importance
of such a parameter sweep. We categorize these 12 parameters into
time-sensitive parameters and time-friendly parameters. Moreover, through
multi-objective analysis, this paper provides an insightful basis for further
hyper-parameter optimization.
