Answers.Body: <p>I think there is an implicit notion of it in dynamic programming; say, if you have to make some sort of search over a subset of a state space and you are deciding whether to use BFS, <em>breath first search</em>, or DFS, <em>depth first search</em>, you are at least implicitly thinking on the best way to explore/exploit the state space.</p>
  <p>As for model based RL, yes. There is explicit algorithms that mediate exploration and exploitation. One of them is UCB, <em>uper confidence bound</em>. One of the best examples of a model based reinforcement learning algorithm is AlphaGo. The algorithm uses a variation of UCB to explore the state space.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-26T18:54:34.407Z
Answers.LastActivityDate: 2021-07-26T18:54:34.407Z
Answers.UserDisplayName: João Schapke
Body: <p>Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-26T15:13:05.287Z
Comments.Text: Given that this is a one-line question, which may be fine, it may be a good idea to explain why you're asking this question or wondering this.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-26T15:18:59.753Z
Comments.Text: Not in dynamic programming no. If you have an unknown model then yes. The issue of RL is basically that you don't know the model.
Comments.UserDisplayName: FourierFlux
Tags: <model-based-methods><exploration-exploitation-tradeoff><dynamic-programming>
Title: Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?
UserDisplayName: user529295
ThreadText: # Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?
  
  (Asked by: username_0 on 2021-07-26)
  
  Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?
  
  **Tags:** model-based-methods, exploration-exploitation-tradeoff, dynamic-programming
  
  > **Comments:**
  > **username_1**: Given that this is a one-line question, which may be fine, it may be a good idea to explain why you're asking this question or wondering this.  
  > **username_2**: Not in dynamic programming no. If you have an unknown model then yes. The issue of RL is basically that you don't know the model.  
  
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  ## 1 answers
  
  ### Answer by username_3 on 2021-07-26. Score: 1
  
  I think there is an implicit notion of it in dynamic programming; say, if you have to make some sort of search over a subset of a state space and you are deciding whether to use BFS, *breath first search*, or DFS, *depth first search*, you are at least implicitly thinking on the best way to explore/exploit the state space.
  
  As for model based RL, yes. There is explicit algorithms that mediate exploration and exploitation. One of them is UCB, *uper confidence bound*. One of the best examples of a model based reinforcement learning algorithm is AlphaGo. The algorithm uses a variation of UCB to explore the state space.
  
  
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