Body: <p>I am looking at some ideas on exploring an environment using Curiosity Driven Exploration and am being a little skeptical about it. The objective here is to just explore without the need to obtain optimal actions for each state. For this purpose, wouldn't it be better to just use some sampling strategy rather than RL?</p>
  <p>For instance, given a large grid world, I'd like to efficiently find the locations of fire pits and the exit door. One way to go about it is Curiosity Driven RL, but that would be overkill since I am not concerned with learning the optimal actions for each state.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-25T16:22:34.700Z
Comments.Text: It is not clear what kind of answer you are looking for - e.g. whether you are concerned about your specific example of exploring a physical space? What information do you as the developer know about the environment and are willing to use to improve efficiency by coding it into the explorer? A typical RL agent won't "know" that going north then south ends up in the same place, and has to learn it. So if you are willing to code that knowledge into your bot it will already be more efficient at exploring a grid space - at the expense of not being able to learn about different kinds of space.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-25T16:41:05.663Z
Comments.Text: I am looking to explore an arbitrary environment. For now, let's assume its the grid world.
  
  As a developer, I won't specify anything that'll help the agent/search algorithm.
  
  One way RL agents are trained to realize that going north and subsequently south leads to the same place, is by computing a similarity metric. But that is not specifically a RL thing and can be used in other applications. 
  
  Thank you for your comment. I realize my question sounds a little open-ended. Please let me know if you have more questions.
Comments.UserDisplayName: desert_ranger
Tags: <deep-rl><exploration-strategies>
Title: Alternatives to using RL to explore an environment
UserDisplayName: desert_ranger
ThreadText: # Alternatives to using RL to explore an environment
  
  (Asked by: username_0 on 2022-02-25)
  
  I am looking at some ideas on exploring an environment using Curiosity Driven Exploration and am being a little skeptical about it. The objective here is to just explore without the need to obtain optimal actions for each state. For this purpose, wouldn't it be better to just use some sampling strategy rather than RL?
  
  For instance, given a large grid world, I'd like to efficiently find the locations of fire pits and the exit door. One way to go about it is Curiosity Driven RL, but that would be overkill since I am not concerned with learning the optimal actions for each state.
  
  **Tags:** deep-rl, exploration-strategies
  > **Comments:**
  > **username_1**: It is not clear what kind of answer you are looking for - e.g. whether you are concerned about your specific example of exploring a physical space? What information do you as the developer know about the environment and are willing to use to improve efficiency by coding it into the explorer? A typical RL agent won't "know" that going north then south ends up in the same place, and has to learn it. So if you are willing to code that knowledge into your bot it will already be more efficient at exploring a grid space - at the expense of not being able to learn about different kinds of space.  
  > **username_0**: I am looking to explore an arbitrary environment. For now, let's assume its the grid world.  As a developer, I won't specify anything that'll help the agent/search algorithm.  One way RL agents are trained to realize that going north and subsequently south leads to the same place, is by computing a similarity metric. But that is not specifically a RL thing and can be used in other applications.   Thank you for your comment. I realize my question sounds a little open-ended. Please let me know if you have more questions.  
  
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  ## 0 answers
  
  