From adbd2abb384fd1a8c44ef1ca272abb6aa6e70f95 Mon Sep 17 00:00:00 2001
From: Kiran Karkera <shark8me@users.noreply.github.com>
Date: Tue, 9 May 2023 10:49:37 +0530
Subject: [PATCH 1/2] fix for rose/red color tiles naming (#317)

Rename rose to red (coloured) state, since red is the preferred usage (as elsewhere in the document: line 37),
---
 units/en/unit4/advantages-disadvantages.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/units/en/unit4/advantages-disadvantages.mdx b/units/en/unit4/advantages-disadvantages.mdx
index 8a65b8d..d9c3438 100644
--- a/units/en/unit4/advantages-disadvantages.mdx
+++ b/units/en/unit4/advantages-disadvantages.mdx
@@ -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"/>

From e30be856bc205e243a3c352fe3d31ec599c771a0 Mon Sep 17 00:00:00 2001
From: Kiran Karkera <shark8me@users.noreply.github.com>
Date: Tue, 9 May 2023 18:47:59 +0530
Subject: [PATCH 2/2] Update advantages-disadvantages.mdx

Change remaining references to rose states
---
 units/en/unit4/advantages-disadvantages.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/units/en/unit4/advantages-disadvantages.mdx b/units/en/unit4/advantages-disadvantages.mdx
index d9c3438..3739a72 100644
--- a/units/en/unit4/advantages-disadvantages.mdx
+++ b/units/en/unit4/advantages-disadvantages.mdx
@@ -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"/>