Weights for the reward function during the model learning

[AlirezaShamsoshoara]

Hi @jangirrishabh,
This repository is awesome and well-explained. I want to thank you for the great content and code.
And I just have a question regarding the learning.py.
My question is: before training your NN model, you defined some weights in line:

toyCarIRL/learning.py

Line 206 in 2eff036

weights = [ 0.04924175 ,-0.36950358 ,-0.15510825 ,-0.65179867 , 0.2985827 , -0.23237454 , 0.21222881 ,-0.47323531]

and fed them into the carmunk to get the immediate reward and the new state based on the taken action to update the Y vector in the mini-batch process method. I was wondering how you defined the weights (weights for the reward function). Because later, you use this trained model in the toy_car_IRL.py to update the policy and reconstruct the weights for the reward function. So do those weights affect the trained NN model or they are just some random values?