Answers.Body: <p>you might have a problem with large <span class="math-container">$n$</span>, but you can consider every possible allocation as an action, and  do that as a multi-armed bandit problem.</p>
  <p>For example, say you have 2 sensors and 3 spots, then you have:</p>
  <ul>
  <li>action 1: sensors in spots 1 and 2</li>
  <li>action 2: sensors in spots 1 and 3</li>
  <li>action 3: sensors in spots 2 and 3</li>
  </ul>
  <p>then, just run some simulations and figure out the best possible allocation</p>
  
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Answers.CreationDate: 2024-01-03T12:33:33.363Z
Answers.LastActivityDate: 2024-01-03T12:33:33.363Z
Answers.UserDisplayName: Alberto
Body: <p>Assume that I have <span class="math-container">$n$</span> sensors to be placed optimally in <span class="math-container">$N$</span> possible locations where <span class="math-container">$n&lt;&lt;N$</span>.
  How can I model this problem as a multi-armed bandit problem?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-01-01T16:15:03.280Z
Comments.Text: Can you describe what a multi-armed bandit problem is? That's the first step to understand how you can formulate this problem. I'd recommend that you read the related chapter in the "Introduction to Reinforcement Learning" book by Sutton and Barto. This also looks like a homework and you should tag it with [tag:homework].
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><multi-armed-bandits>
Title: Optimal sensor placement formulation as multi-armed bandit
UserDisplayName: DSPinfinity
ThreadText: # Optimal sensor placement formulation as multi-armed bandit
  
  (Asked by: username_0 on 2024-01-01)
  
  Assume that I have $n$ sensors to be placed optimally in $N$ possible locations where $n<.
  How can I model this problem as a multi-armed bandit problem?
  
  **Tags:** reinforcement-learning, multi-armed-bandits
  > **Comments:**
  > **username_1**: Can you describe what a multi-armed bandit problem is? That's the first step to understand how you can formulate this problem. I'd recommend that you read the related chapter in the "Introduction to Reinforcement Learning" book by Sutton and Barto. This also looks like a homework and you should tag it with [tag:homework].  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2024-01-03. Score: 2 | Accepted answer
  
  you might have a problem with large $n$, but you can consider every possible allocation as an action, and do that as a multi-armed bandit problem.
  
  For example, say you have 2 sensors and 3 spots, then you have:
  
  * action 1: sensors in spots 1 and 2
  * action 2: sensors in spots 1 and 3
  * action 3: sensors in spots 2 and 3
  
  then, just run some simulations and figure out the best possible allocation
  
  
  ---
  
  