Weight initialization

[RDTm]

The weights should be initialized in a way that the initial values for expected rewards (when giving input to initial network) would be the same order of magnitude or rather a few orders of magnitude smaller than the reward that we give in case we break a tile (reward=1). At the moment the rewards at the randomly initialized network go as far as (-200 or +200).
We need to decrease weight values, because then adding a reward of 1 to a desired transition/state would really make us choose this same transition next time.

this should be done in constructors of individual layers (the way we initialize W and B)

also, Biases are all initialized at zero for the moment. need to change that.