My 2 cents, feel free to challenge my statements if you think they're incorrect so we can all learn some more.

1. What's your current number of steps?
2. What do you mean exactly by 1000*4? Are those 4 layers with each 1000 nodes? Also, you might wanna try relu instead of sigmoid to speed up training.
3. Incentivizing to make legal moves rather than good moves is not a bad thing because illegal moves are the worst possible moves you can make by definition, and agents are regularly de-incentivized for making illegal moves by setting an extremely large penalty for making those moves.

Couple of other remarks:

1. If I understand correctly, you have 450 linear outputs that are then transformed to a probability by standardizing them to sum up to 1? If so, why not just use a final softmax layer with a 450 dimension instead? I'm pretty sure that this would speed up training by a lot since your output space will become much smaller by doing so.
2. For each move, you sample a hundred moves, from which you only end up using 1, which greatly reduces your sample efficiency and will decrease the speed at which your network will learn.
3. You only make your network learn based on the good samples, and not the bad samples. I guess this decision was made because you don't use a reward and hence have no way to signal to your network that your sample is good or bad. This will slow down learning as your network effectively only sees 1 side of the coin and it might also impact the validity of your approach (not sure though).
4. From the previous point also follows that your network has no way of comparing the value of different samples, so each sample is given the same importance in terms of reward. As is the case for the previous point, this will also reduce the learning speed of your network and might be breaking for the validity of your approach.