Here is a paper you might be interested in:


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 
6×6 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.


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