diff --git a/reinforcement-learning/breakout/DQN.py b/reinforcement-learning/breakout/DQN.py
index fff4ff0..ee5c6b1 100644
--- a/reinforcement-learning/breakout/DQN.py
+++ b/reinforcement-learning/breakout/DQN.py
@@ -275,7 +275,7 @@ def preprocess(self, state):
 
         return state
 
-    def observe(self, environment, states, *args):  # noqa
+    def observe(self, environment, states, skip=None):
         """
         Observe the environment for n frames.
 
@@ -285,7 +285,7 @@ def observe(self, environment, states, *args):  # noqa
             The environment to observe.
         states : torch.Tensor
             The states of the environment from the previous step.
-        args
+        skip : int, optional
             To be compatible with the other DQN agents. Added here instead of using ABC.
 
         Returns
@@ -299,6 +299,8 @@ def observe(self, environment, states, *args):  # noqa
         done : bool
             Whether the game is terminated.
         """
+        print("Warning: `skip` is not used in `VisionDeepQ.observe`.") if skip is not None else None
+
         action = self.action(states)
 
         done = False
diff --git a/reinforcement-learning/utilities/visualisation/movie.py b/reinforcement-learning/utilities/visualisation/movie.py
index ae05770..9e45626 100644
--- a/reinforcement-learning/utilities/visualisation/movie.py
+++ b/reinforcement-learning/utilities/visualisation/movie.py
@@ -1,8 +1,10 @@
+"""Create a movie of an agent interacting with an environment."""
+
 import cv2
 import torch
 
 
-def create_movie(environment, agent, path, fps=60):
+def create_movie(environment, agent, path="./live-preview.gif", skip=4, fps=50):
     """Created by Mistral Large."""
     initial = agent.preprocess(environment.reset()[0])
     try:
@@ -10,20 +12,14 @@ def create_movie(environment, agent, path, fps=60):
     except AttributeError:
         states = initial
 
-    try:
-        done = False
+    done = False
 
-        # Get the dimensions of the first image
-        height, width, channels = environment.render().shape
+    height, width, _ = environment.render().shape
+    fourcc = cv2.VideoWriter_fourcc(*"MJPG")  # noqa
+    movie = cv2.VideoWriter(path, fourcc, fps, (width, height))
 
-        # Create the VideoWriter object
-        fourcc = cv2.VideoWriter_fourcc(*"MJPG")  # You can change the codec if needed
-        video_writer = cv2.VideoWriter(path, fourcc, fps, (width, height))
-        while not done:
-            _, states, _, done = agent.observe(environment, states)
-            video_writer.write(environment.render())
-    except Exception as e:
-        print(f"Error during image generation or writing: {e}")
-        return
+    while not done:
+        _, states, _, done = agent.observe(environment, states, skip)
+        movie.write(environment.render())
 
     cv2.destroyAllWindows()
diff --git a/requirements.txt b/requirements.txt
index 85038d8..c382e43 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,8 +8,12 @@ torch
 numpy
 pandas
 scipy
+
+# For visualisation
+# *-----------------------------------------*
 matplotlib
 imageio
+opencv-python
 
 # Library for utilising Apple M chips
 # *-----------------------------------------*