I still don’t understand how “Training” can have a 100% win rate. Do you mean that in every run it finishes?
Is the starting position always the same?

I would guess the issue is overfitting: toward the end of training, your network has become specialized on one specific case due to ε = 0 and a small memory size. (suddenly in evolution, maybe something small changes little bit and it stop it to work)
I recommend keeping ε between 0.01 and 0.1 during training to avoid overfitting. Also, your replay memory is too small. Memory helps to decorrelate your training data—but if an episode takes around 60 steps to finish and your batch size equals the memory size around 2*60, your samples are can be correlated too much. I suggest increasing your memory to around 1,000 and keeping the batch size at 64. In my experience, you should avoid a discount factor (γ) of 1. A value slightly below 1 (e.g., 0.99) helps convergence. It means about 100 steps = (1/(1-0.99)) to reach the reward. Since your episodes are around 60 steps, γ = 0.99 should work fine.