I suppose varying the neural net size wouldn't be the best way of doing that; very small nets can have very "unhuman-like" behaviour. I'm not an expert on reinforcement learning, but for other fields in deep learning that's typically the case.

I think that, to simulate worse human-like players, it would be better to just increase the temperature: don't always select the best move, at every step just select one of the top 10, randomly proportional to some function of the model-predicted probability of it being "the best" move (e.g. a power of the probability; very large powers give always the best move, i.e. the strongest player, and powers close to 0 tend to choose uniformly at random, i.e. the weakest player). The only thing I'm not certain about is, if you train the original network well enough, stupid blunders (that a very bad human player like me would make) are still scored so low that there's no way this algorithm will pick them up - the only way to know would be to try.