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LearnDataSci · Jul 11, 2018 · 93718ea · 93718ea
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diff --git a/Intro to Reinforcement Q-Learning/A_practical_introduction_to_Reinforcement_Learning.ipynb b/Intro to Reinforcement Q-Learning/A_practical_introduction_to_Reinforcement_Learning.ipynb
diff --git a/Intro to Reinforcement Q-Learning/assets/Reinforcement Learning Taxi Env.png b/Intro to Reinforcement Q-Learning/assets/Reinforcement Learning Taxi Env.png
diff --git a/Intro to Reinforcement Q-Learning/assets/Taxi-v2 render 1.png b/Intro to Reinforcement Q-Learning/assets/Taxi-v2 render 1.png
diff --git a/Intro to Reinforcement Q-Learning/assets/q matrix initialized.png b/Intro to Reinforcement Q-Learning/assets/q matrix initialized.png
diff --git a/Intro to Reinforcement Q-Learning/assets/q-matrix-after-run.png b/Intro to Reinforcement Q-Learning/assets/q-matrix-after-run.png
diff --git a/Intro to Reinforcement Q-Learning/assets/q-matrix-initialized-to-learned.png b/Intro to Reinforcement Q-Learning/assets/q-matrix-initialized-to-learned.png
diff --git a/Intro to Reinforcement Q-Learning/assets/q-matrix-initialized.png b/Intro to Reinforcement Q-Learning/assets/q-matrix-initialized.png
diff --git a/Intro to Reinforcement Q-Learning/utils.py b/Intro to Reinforcement Q-Learning/utils.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+from IPython.display import clear_output
+from time import sleep
+
+def print_frames(frames):
+    for frame in frames:
+        clear_output(wait=True)
+        print(frame['frame'].getvalue())
+        print(f"State: {frame['state']}")
+        print(f"Reward: {frame['reward']}")
+        sleep(.1)
+
+
+def plot_q_table(q_table):
+    fig, ax = plt.subplots(figsize=(16,16))
+    ax = sns.heatmap(q_table, cmap="Blues")     
+    plt.show()
+
+
+def display_training_info(episode, epochs):
+	pass
+
+