Is feature scaling necessary in reinforcement learning for the agent to learn successfully? I often have trouble deciding how to tweak my input data for the agent. I am changing my data so that values in the hundreds get changed to a value between -1 and 1, and values that are originally small get changed the same ratio. However, my agents often aren't successful. 

Could the numeric range of the input data be a cause for the inability to learn?
I assume that when you say


  values that are originally small get changed the same ratio


you are standardizing your features so that every dimension is on the same scale from [-1,1]

This is common practice as it helps many models converge faster.  It would not prevent your models from generalizing the data, so here are some other possible causes for your problem:


The ML algorithm you are using is not appropriate for your task
The data is either insufficient in quantity or quality (e.g. noisy data)
The preprocessing stage is lacking some steps such as dimensionality reduction