Updated lines 80-147 with: st.write("Example 3 via Method 1:") # Example 3 parameters eta = 0.1 N_st = 3 epsilon = 1e-5 # Intrinsic reward formulation r_t_int = eta * (1 / (N_st + epsilon)**0.5) # Display the formulation with parameters plugged in st.latex(r""" r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{3 + 1 x 10^{-5}}} """) st.write(f"Calculated intrinsic reward: {r_t_int}") st.dataframe(df_0_0[2:3]) # Display the formulation with parameters plugged in st.write("Example 4 via Method 2:") st.latex(r""" r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{4 + 1 x 10^{-5}}} """) # Abstract Base Class for Intrinsic Reward Calculation class IntrinsicRewardCalculator(ABC): @abstractmethod def calculate_intrinsic_reward(self, eta, count, epsilon): pass # Concrete Class for Intrinsic Reward Calculation class ConcreteIntrinsicRewardCalculator(IntrinsicRewardCalculator): def calculate_intrinsic_reward(self, eta, count, epsilon): return eta * (1 / np.sqrt(count + epsilon)) def populate_df_0_0(self, df_0_0, eta, count, epsilon): intrinsic_reward = self.calculate_intrinsic_reward(eta, count, epsilon) df_0_0.at[0, 'Intrinsic Reward'] = intrinsic_reward return df_0_0 # Example 4 parameters eta = 0.1 count = 4 epsilon = 1e-5 x,y = 0,0 # Create instance for Intrinsic Reward Calculation irc = ConcreteIntrinsicRewardCalculator() intrinsic_reward = irc.calculate_intrinsic_reward(0.1, 4, 1e-5) st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y}:", intrinsic_reward) st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y} rounded 2 decimal places:", np.round(intrinsic_reward,2)) # Populate the DataFrame with the calculated intrinsic reward df_0_0 = irc.populate_df_0_0(df_0_0, eta, count, epsilon) # Display the updated DataFrame st.dataframe(df_0_0[3:4]) st.write("Example 5 via Method 1:") # Example 5 parameters eta = 0.1 N_st = 5 epsilon = 1e-5 # Intrinsic reward formulation r_t_int = eta * (1 / (N_st + epsilon)**0.5) # Display the formulation with parameters plugged in st.latex(r""" r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{5 + 1 x 10^{-5}}} """) st.write(f"Calculated intrinsic reward: {r_t_int}") st.write(f"Calculated intrinsic reward rounded 4 decimal places:", np.round(r_t_int,4)") st.dataframe(df_0_0[4:5])

app.py

@@ -20,24 +20,21 @@ st.write("Oh, sweet Homer's doughnuts! If that second .wav file ain't playin', j
 st.audio("h2.wav", format="audio/wav")
 st.image("intrinsic_reward_formulation.png", caption='Intrinsic Reward Formulation')
 st.write("Solving the first 5 equations @ (0,0):")
-st.write("Example 1 via Method 1:")
 
 df_0_0 = pd.read_csv('df_0_0.csv')
 st.write(df_0_0.shape)
 
+st.write("Example 1 via Method 1:")
 # Define parameters
 eta = 0.1
 N_st = 1
 epsilon = 1e-5
-
 # Intrinsic reward formulation
 r_t_int = eta * (1 / (N_st + epsilon)**0.5)
-
 # Display the formulation with parameters plugged in
 st.latex(r"""
 r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{1 + 1 x 10^{-5}}}
 """)
-
 st.write(f"Calculated intrinsic reward: {r_t_int}")
 st.dataframe(df_0_0[:1])
 
@@ -48,7 +45,6 @@ st.latex(r"""
 r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{2 + 1 x 10^{-5}}}
 """)
 
-
 # Abstract Base Class for Intrinsic Reward Calculation
 class IntrinsicRewardCalculator(ABC):  
     @abstractmethod  
@@ -74,18 +70,81 @@ x,y = 0,0
 # Create instance for Intrinsic Reward Calculation
 irc = ConcreteIntrinsicRewardCalculator()
 intrinsic_reward = irc.calculate_intrinsic_reward(0.1, 2, 1e-5)
-
-
 st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y}:", intrinsic_reward)
 st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y} rounded 4 decimal places:", np.round(intrinsic_reward,4))
+# Populate the DataFrame with the calculated intrinsic reward
+df_0_0 = irc.populate_df_0_0(df_0_0, eta, count, epsilon)
+# Display the updated DataFrame
+st.dataframe(df_0_0[1:2])
+
+st.write("Example 3 via Method 1:")
+# Example 3 parameters
+eta = 0.1
+N_st = 3
+epsilon = 1e-5
+# Intrinsic reward formulation
+r_t_int = eta * (1 / (N_st + epsilon)**0.5)
+# Display the formulation with parameters plugged in
+st.latex(r"""
+r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{3 + 1 x 10^{-5}}}
+""")
+st.write(f"Calculated intrinsic reward: {r_t_int}")
+st.dataframe(df_0_0[2:3])
 
 
+
+# Display the formulation with parameters plugged in
+st.write("Example 4 via Method 2:")
+st.latex(r"""
+r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{4 + 1 x 10^{-5}}}
+""")
+
+# Abstract Base Class for Intrinsic Reward Calculation
+class IntrinsicRewardCalculator(ABC):  
+    @abstractmethod  
+    def calculate_intrinsic_reward(self, eta, count, epsilon):    
+        pass
+        
+# Concrete Class for Intrinsic Reward Calculation
+class ConcreteIntrinsicRewardCalculator(IntrinsicRewardCalculator):  
+    def calculate_intrinsic_reward(self, eta, count, epsilon):    
+        return eta * (1 / np.sqrt(count + epsilon))  
+    
+    def populate_df_0_0(self, df_0_0, eta, count, epsilon):        
+        intrinsic_reward = self.calculate_intrinsic_reward(eta, count, epsilon)       
+        df_0_0.at[0, 'Intrinsic Reward'] = intrinsic_reward        
+        return df_0_0
+
+# Example 4 parameters
+eta = 0.1
+count = 4
+epsilon = 1e-5
+x,y = 0,0
+
+# Create instance for Intrinsic Reward Calculation
+irc = ConcreteIntrinsicRewardCalculator()
+intrinsic_reward = irc.calculate_intrinsic_reward(0.1, 4, 1e-5)
+st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y}:", intrinsic_reward)
+st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y} rounded 2 decimal places:", np.round(intrinsic_reward,2))
 # Populate the DataFrame with the calculated intrinsic reward
 df_0_0 = irc.populate_df_0_0(df_0_0, eta, count, epsilon)
-
 # Display the updated DataFrame
-st.dataframe(df_0_0[1:2])
+st.dataframe(df_0_0[3:4])
 
+st.write("Example 5 via Method 1:")
+# Example 5 parameters
+eta = 0.1
+N_st = 5
+epsilon = 1e-5
+# Intrinsic reward formulation
+r_t_int = eta * (1 / (N_st + epsilon)**0.5)
+# Display the formulation with parameters plugged in
+st.latex(r"""
+r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{5 + 1 x 10^{-5}}}
+""")
+st.write(f"Calculated intrinsic reward: {r_t_int}")
+st.write(f"Calculated intrinsic reward rounded 4 decimal places:", np.round(r_t_int,4)")
+st.dataframe(df_0_0[4:5])