I want to use a Deep Q-Network for a specific problem. My immediate rewards ($r_t = 0$) are all zeros. But my terminal reward is a large positive value $(r_T=100$). How could I normalize rewards to stabilize the training? I think clipping rewards to be in range $[0,1]$ makes training harder because it just forces most values to be near zero.