# How to normalize rewards in DQN?

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.

• why do you need to scale the reward here? the problem I would be concerned with is that almost all of your rewards are non-zero. sparse reward environments can be difficult to solve. perhaps you should look at the Hindsight Experience Replay algorithm as a starting point for overcoming sparse rewards. Dec 28 '21 at 23:12
• Thank you. I now understand what I should do. I need to Define a bigger state which contains smaller state and gradually decrease it's volume so at the end of training it is equal to smaller state. Dec 29 '21 at 7:51