Updated lines 45-86 with: # Display the formulation with parameters plugged in st.write("Example 2 via Method 2:") 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 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 2 parameters eta = 0.1 count = 2 epsilon = 1e-5 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 6 decimal places:", np.round(intrinsic_reward,6)) # 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.write(df_0_0[1:2])

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 import plotly.graph_objects as go
 import plotly.express as px
 import numpy as np
+from abc import ABC, abstractmethod
 
 
 # Set the title of the app
@@ -32,7 +33,7 @@ epsilon = 1e-5
 # Intrinsic reward formulation
 r_t_int = eta * (1 / (N_st + epsilon)**0.5)
 
-# Display the formulat with parameters plugged in
+# 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}}}
 """)
@@ -41,7 +42,48 @@ st.write(f"Calculated intrinsic reward: {r_t_int}")
 st.dataframe(df_0_0[:1])
 
 
+# Display the formulation with parameters plugged in
+st.write("Example 2 via Method 2:")
+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  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 2 parameters
+eta = 0.1
+count = 2
+epsilon = 1e-5
+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 6 decimal places:", np.round(intrinsic_reward,6))
+
+
+# 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.write(df_0_0[1:2])