diff --git a/tutorials/W3D4_ReinforcementLearning/W3D4_Tutorial3.ipynb b/tutorials/W3D4_ReinforcementLearning/W3D4_Tutorial3.ipynb
index ed042cc67a..231b8a8425 100644
--- a/tutorials/W3D4_ReinforcementLearning/W3D4_Tutorial3.ipynb
+++ b/tutorials/W3D4_ReinforcementLearning/W3D4_Tutorial3.ipynb
@@ -61,12 +61,12 @@
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@@ -125,11 +125,11 @@
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@@ -490,7 +490,7 @@
         "\n",
         "For our discussion we will be looking at the classic Cliff World, or Cliff Walker, environment. This is a 4x10 grid with a starting position in the lower-left and the goal position in the lower-right. Every tile between these two is the \"cliff\", and should the agent enter the cliff, they will receive a -100 reward and be sent back to the starting position. Every tile other than the cliff produces a -1 reward when entered. The goal tile ends the episode after taking any action from it.\n",
         "\n",
-        "<img alt=\"CliffWorld\" width=\"577\" height=\"308\" src=\"https://github.com/NeuromatchAcademy/course-content/blob/main/tutorials/static/W3D4_Tutorial3_CliffWorld.png?raw=true\">\n",
+        "<img alt=\"CliffWorld\" width=\"577\" height=\"308\" src=\"https://github.com/NeuromatchAcademy/course-content/blob/main/tutorials/static/W3D4_Tutorial3_GridWorld410.png?raw=true\">\n",
         "\n",
         "Given these conditions, the maximum achievable reward is -11 (1 up, 9 right, 1 down). Using negative rewards is a common technique to encourage the agent to move and seek out the goal state as fast as possible."
       ]
@@ -756,12 +756,12 @@
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@@ -1049,12 +1049,12 @@
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@@ -1142,8 +1142,6 @@
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@@ -1303,12 +1303,12 @@
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