How to scale all positive continuous reward? My RL project has all positive continuous rewards for every step and the goal is to have the maximum cumulative reward (episodic reward). The problem is that the rewards are too close and all between 5 and 6, therefore achieving the optimum episodic reward will be harder.
What scaling methods are recommended? (like min-max scaling or reward ** 3)
How can I emphasize the episodic reward?
I’ll try to find where I found it, but normalizing the rewards has always worked for me. Assuming you have a list of the discounted returns for each action, you subtract the whole list by its average value then divide it by its standard deviation. In Python with NumPy, that would look like:
returns -= np.mean(returns)
returns /= np.std(returns)

This puts the returns in a small and consistent range that keeps learning similar with different rewards.