Body: <p>I'm training an RL agent using the DQN algorithm to do a specific task. The environment is represented by a list of <span class="math-container">$10$</span> integer numbers from <span class="math-container">$0$</span> to <span class="math-container">$20$</span>. An example would be <span class="math-container">$[5, 15, 8, 8, 0, \dots]$</span>.</p>
  <p>Is it okay to pass the list as floats to the dense layer, or would that impede the learning process? What is the right way to go about passing integer numbers to the neural network?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-16T12:56:42.517Z
Comments.Text: It looks like 10 numbers, it's not that much as input for dense layers. I would recommend normalizing them either to float in the range from 0 to 1 or [-1;1] depending on data specifics.
Comments.UserDisplayName: Stepan Novikov
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-17T00:45:50.983Z
Comments.Text: This seems to help my model's performance!
Comments.UserDisplayName: mark mark
Tags: <neural-networks><reinforcement-learning><environment>
Title: What is the appropriate way of passing a list of integers that represents the environment to a neural network's dense layer?
UserDisplayName: mark mark
ThreadText: # What is the appropriate way of passing a list of integers that represents the environment to a neural network's dense layer?
  
  (Asked by: username_0 on 2020-11-15)
  
  I'm training an RL agent using the DQN algorithm to do a specific task. The environment is represented by a list of $10$ integer numbers from $0$ to $20$. An example would be $[5, 15, 8, 8, 0, \dots]$.
  
  Is it okay to pass the list as floats to the dense layer, or would that impede the learning process? What is the right way to go about passing integer numbers to the neural network?
  
  **Tags:** neural-networks, reinforcement-learning, environment
  > **Comments:**
  > **username_1**: It looks like 10 numbers, it's not that much as input for dense layers. I would recommend normalizing them either to float in the range from 0 to 1 or [-1;1] depending on data specifics.  
  > **username_0**: This seems to help my model's performance!  
  
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  ## 0 answers
  
  