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Merge pull request #321 from shark8me/patch-1

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fix for rose/red color tiles naming (#317)
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Thomas Simonini
2023-05-09 17:24:49 +02:00
committed by GitHub
parent de95427bbc e30be856bc
commit 377e8c1bf4
1 changed files with 2 additions and 2 deletions
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units/en/unit4/advantages-disadvantages.mdx
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@@ -28,7 +28,7 @@ Let's take an example: we have an intelligent vacuum cleaner whose goal is to su
Our vacuum cleaner can only perceive where the walls are.
The problem is that the **two rose cases are aliased states because the agent perceives an upper and lower wall for each**.
The problem is that the **two red (colored) states are aliased states because the agent perceives an upper and lower wall for each**.
<figure class="image table text-center m-0 w-full">
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/hamster2.jpg" alt="Hamster 1"/>
@@ -38,7 +38,7 @@ Under a deterministic policy, the policy will either always move right when in a
Under a value-based Reinforcement learning algorithm, we learn a **quasi-deterministic policy** ("greedy epsilon strategy"). Consequently, our agent can **spend a lot of time before finding the dust**.
On the other hand, an optimal stochastic policy **will randomly move left or right in rose states**. Consequently, **it will not be stuck and will reach the goal state with a high probability**.
On the other hand, an optimal stochastic policy **will randomly move left or right in red (colored) states**. Consequently, **it will not be stuck and will reach the goal state with a high probability**.
<figure class="image table text-center m-0 w-full">
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/hamster3.jpg" alt="Hamster 1"/>