Paper:
Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go. However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process. In this research, the coordination of episode generation was focused on. By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation. The experiments with a small problem showed that it had robust performance compared to the existing method, Alpha Zero.

Review:
Badly and unprofessionally written
I had and still have a hard time to report what this paper is about. Generally, I can tell probably this paper study the Monte Carlo Tree Search-based methods in the field of Reinforcement Learning. 
The paper is badly written with grammatical issues almost in all the sentences. I definitely urge the author(s) to take the writing significantly more seriously. I almost all the time do not end up rejecting a paper due to the lack of clear writing, but for this paper, unfortunately, despite spending time, I could not understand the paper.
Comments beside the grammatical issues:
I am sorry but I could not understand the abstract.
What does the author(s) mean by "In this research, the
coordination of episode generation was focused on." Same issue with almost all the sentences in the abstract. Unfortunately, I could not follow any of them. Moreover, this paper is full of inaccurate and unjustified statements. Furthermore, again, unfortunately, this paper does not represent even the preliminary elements of scientific writing.
I decided to provide further comments, but sadly I had to change my mind because otherwise, I had to write at least one paragraph of explanation for each paragraph of this paper. With that regard, I do not think it is an appropriate way to utilize the review process. But I would be happy to provide more abstract comments upon request.