<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>

CC BY-SA 3.0
2017-10-27T02:45:39.540Z
Have you tried adding an additional, binary feature indicating for each reward if it has already been caught?
user12889
CC BY-SA 3.0
2017-10-27T08:08:20.297Z
@user12889 Great idea, actually, I hadn't thought of that
BlueMoon93
<neural-networks><reinforcement-learning>
Handling varied-size input with fixed-input network
BlueMoon93
# 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  

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## 0 answers

