Is feature scaling necessary in reinforcement learning for the agent to learn successfully?
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