<p>Your two suggestions are not mutually exclusive. If you go by this process, you'll have to do a &quot;Cartesian product&quot; of a bunch of different RL categorizations which would get out of hand. I recommend, if you can, to describe some sort of &quot;RL taxonomy&quot; instead. By this I mean describing different RL characterizations without assuming they're mutually exclusive.</p>
<p>To add to your list :</p>
<ul>
<li>On-policy or off-policy</li>
<li>Value based or policy gradient</li>
</ul>

CC BY-SA 4.0
2020-07-03T21:42:42.563Z
Maybe you could include a diagram like [this one](https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html). It's your choice, but sometimes diagrams help.
nbro
CC BY-SA 4.0
2020-07-04T00:07:30.267Z
+1 for the diagram nbro posted, that's a great visualization for what I was trying to describe
harwiltz
CC BY-SA 4.0
2020-07-03T12:49:31.310Z
2020-07-03T12:49:31.310Z
harwiltz
<p>In the course of a scientific work, I will discuss the different types of reinforcement learning. However, I have difficulties to find these different types.</p>
<p>So, into which subcategories can reinforcement learning be divided? For example, the following subdivisions seem to be useful</p>
<ul>
<li>Model-free and Model-based</li>
<li>Dynamic Programming, Monte Carlo and Temporal Difference</li>
</ul>
<p>Any others?</p>

<reinforcement-learning><monte-carlo-methods><temporal-difference-methods><model-based-methods><model-free-methods>
Into which subcategories can reinforcement learning be divided?
jackless
# Into which subcategories can reinforcement learning be divided?

(Asked by: username_0 on 2020-07-03)

In the course of a scientific work, I will discuss the different types of reinforcement learning. However, I have difficulties to find these different types.

So, into which subcategories can reinforcement learning be divided? For example, the following subdivisions seem to be useful

* Model-free and Model-based
* Dynamic Programming, Monte Carlo and Temporal Difference

Any others?

**Tags:** reinforcement-learning, monte-carlo-methods, temporal-difference-methods, model-based-methods, model-free-methods


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## 1 answers

### Answer by username_1 on 2020-07-03. Score: 4 | Accepted answer

Your two suggestions are not mutually exclusive. If you go by this process, you'll have to do a "Cartesian product" of a bunch of different RL categorizations which would get out of hand. I recommend, if you can, to describe some sort of "RL taxonomy" instead. By this I mean describing different RL characterizations without assuming they're mutually exclusive.

To add to your list :

* On-policy or off-policy
* Value based or policy gradient

> **Comments:**
> **username_2**: Maybe you could include a diagram like [this one](https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html). It's your choice, but sometimes diagrams help.  
> **username_1**: +1 for the diagram username_2 posted, that's a great visualization for what I was trying to describe  

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