<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>

CC BY-SA 4.0
2021-07-26T18:54:34.407Z
2021-07-26T18:54:34.407Z
João Schapke
<p>Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?</p>

CC BY-SA 4.0
2021-07-26T15:13:05.287Z
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.
nbro
CC BY-SA 4.0
2021-07-26T15:18:59.753Z
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.
FourierFlux
<model-based-methods><exploration-exploitation-tradeoff><dynamic-programming>
Is there a notion of exploration-exploitation tradeoff in dynamic programming (or model-based RL)?
user529295
# 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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