Modify evaluation score processing #4
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So that it does not record NaN. Now gif is always generated after an evaluation.
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So that it does not record NaN. Now gif is always generated after an evaluation.
For the case you do not want this pull request, I divided this pr from the reward clipping one #3.
I think the gif file size could be a problem. When I tested, if the agent got stuck in a loop, the size of a gif file created is about 180 MB big. In worst case, the disk usage for gif files would be about 150 * 180 MB = 27 GB. And I think maybe in average case it is about 16GB (On my training, 78 of 150 was the unfinished first evaluation game case, and rest 72 was the finished case whose gif files have disk usage about 1.7GB. So, 78 * 180 MB + 1.7 GB = 14GB + 1.7 GB = about 16 GB).
It would be not a problem when a machine has a sufficient disk space. Considering tensorflow save model files have disk usage about 12GB, over 28GB disk space is required to run the train code.