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
  • $\begingroup$ Thanks for the answer. Does this apply to Reinforcement Learning too? $\endgroup$ Apr 25 '18 at 11:55
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    $\begingroup$ Reinforcement learning is a very broad concept, so one cannot make an absolute statement about my answer's application to reinforcement learning as a whole. If you provide more details about your dataset, objective, and the ML algorithms you are experimenting with, I can probably give more helpful advice. $\endgroup$ Apr 25 '18 at 12:13

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