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Update advantages-disadvantages.mdx

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Change remaining references to rose states
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Kiran Karkera
2023-05-09 18:47:59 +05:30
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units/en/unit4/advantages-disadvantages.mdx
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@@ -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"/>