Answers.Body: <p>I think this question is hinting at the problem of choosing an <strong>exploration strategy</strong>.</p>
  <p>The simplest strategy is to use the so called <em>epsilon-greedy strategy</em> (or <span class="math-container">$\epsilon$</span>-greedy). This means that you select an action at random <span class="math-container">$x$</span> percent of the times that an agent has to select an action. The other times, the agent takes the action that its current policy dictates. Usually, <span class="math-container">$x$</span> declines throughout the learning process.</p>
  <p>There are many different exploration strategies to choose from, with the aforementioned one being really the most simple one. For getting an impression of how diverse these different approaches to exploration strategies are, please be referred to Google Scholar (searching for &quot;reinforcement learning exploration strategies&quot;).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-22T11:13:15.933Z
Answers.Text: It might be right, but I think you may have jumped in too early with an answer here. It is not clear to me that the op is asking about exploration strategy, they appear to be stuck earlier in the design process
Answers.UserDisplayName: Neil Slater
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-29T17:29:07.270Z
Answers.LastActivityDate: 2020-09-29T17:29:07.270Z
Answers.UserDisplayName: Daniel B.
Body: <p>I have an agent (drone) that has to allocate subchannels for different types of User Equipment.</p>
  <p>I have represented the subchannel allocation with a 2-dimentional binary matrix, that is initialized to all zeros as there is no requests at the beginning of the episode.</p>
  <p>When the agent chooses an action, it has to choose which subchannels to allocate to which UEs, hence populating the matrix with 1s.</p>
  <p>I have no idea how to do it.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-28T09:47:31.883Z
Comments.Text: Can you please be a little more intuitive on what you would like to do? What is a subchannel, UE, and in general what a human being would face in this problem. Describe a use case scenario. This would help to define our env, space and actions.
Comments.UserDisplayName: ddaedalus
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-22T11:10:41.627Z
Comments.Text: To extend ddaedalus' comment, it would help to understand what the goals of the allocation are, and what the difference is between good and bad allocations
Comments.UserDisplayName: Neil Slater
Tags: <reinforcement-learning><python><environment>
Title: How to let the agent choose how to populate a state space matrix in RL (using python)
UserDisplayName: Ness
ThreadText: # How to let the agent choose how to populate a state space matrix in RL (using python)
  
  (Asked by: username_0 on 2020-09-27)
  
  I have an agent (drone) that has to allocate subchannels for different types of User Equipment.
  
  I have represented the subchannel allocation with a 2-dimentional binary matrix, that is initialized to all zeros as there is no requests at the beginning of the episode.
  
  When the agent chooses an action, it has to choose which subchannels to allocate to which UEs, hence populating the matrix with 1s.
  
  I have no idea how to do it.
  
  **Tags:** reinforcement-learning, python, environment
  > **Comments:**
  > **username_1**: Can you please be a little more intuitive on what you would like to do? What is a subchannel, UE, and in general what a human being would face in this problem. Describe a use case scenario. This would help to define our env, space and actions.  
  > **username_2**: To extend ddaedalus' comment, it would help to understand what the goals of the allocation are, and what the difference is between good and bad allocations  
  
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  ## 1 answers
  
  ### Answer by username_3 on 2020-09-29. Score: -1
  
  I think this question is hinting at the problem of choosing an **exploration strategy**.
  
  The simplest strategy is to use the so called *epsilon-greedy strategy* (or $\epsilon$-greedy). This means that you select an action at random $x$ percent of the times that an agent has to select an action. The other times, the agent takes the action that its current policy dictates. Usually, $x$ declines throughout the learning process.
  
  There are many different exploration strategies to choose from, with the aforementioned one being really the most simple one. For getting an impression of how diverse these different approaches to exploration strategies are, please be referred to Google Scholar (searching for "reinforcement learning exploration strategies").
  
  > **Comments:**
  > **username_2**: It might be right, but I think you may have jumped in too early with an answer here. It is not clear to me that the op is asking about exploration strategy, they appear to be stuck earlier in the design process  
  
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