Body: <p>I'm running A3C (Asynchronous Actor-Critic Agents) to learn a game where an agent needs to catch 3 rewards. The input of my network, among other things, is the relative position of the 3 rewards against the agent. </p>
  
  <p>However, as the agent catches these rewards, my network can't just decrease its input size. What are ways of handling this?</p>
  
  <p>My current solutions have been:</p>
  
  <ul>
  <li><p>set remaining features to zero - have tried, bad results</p></li>
  <li><p>just show the closest reward - loss of information, might not be optimal</p></li>
  <li><p>repeat existing reward positions to remaining features - we are giving more emphasis to some rewards (the ones that are repeated) over others</p></li>
  </ul>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-27T02:45:39.540Z
Comments.Text: Have you tried adding an additional, binary feature indicating for each reward if it has already been caught?
Comments.UserDisplayName: user12889
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-10-27T08:08:20.297Z
Comments.Text: @user12889 Great idea, actually, I hadn't thought of that
Comments.UserDisplayName: BlueMoon93
Tags: <neural-networks><reinforcement-learning>
Title: Handling varied-size input with fixed-input network
UserDisplayName: BlueMoon93
ThreadText: # Handling varied-size input with fixed-input network
  
  (Asked by: username_0 on 2017-10-25)
  
  I'm running A3C (Asynchronous Actor-Critic Agents) to learn a game where an agent needs to catch 3 rewards. The input of my network, among other things, is the relative position of the 3 rewards against the agent.
  
  However, as the agent catches these rewards, my network can't just decrease its input size. What are ways of handling this?
  
  My current solutions have been:
  
  * set remaining features to zero - have tried, bad results
  * just show the closest reward - loss of information, might not be optimal
  * repeat existing reward positions to remaining features - we are giving more emphasis to some rewards (the ones that are repeated) over others
  
  **Tags:** neural-networks, reinforcement-learning
  
  > **Comments:**
  > **username_1**: Have you tried adding an additional, binary feature indicating for each reward if it has already been caught?  
  > **username_0**: @username_1 Great idea, actually, I hadn't thought of that  
  
  ---
  
  ## 0 answers
  
  