Updated line 92 with: st.write(f"Calculated intrinsic reward rounded 4 decimal places:", np.round(r_t_int,4))

Updated line 146 with: st.write(f"Calculated intrinsic reward rounded 4 decimal places:", np.round(r_t_int,4))

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])

Updated lines 80, 87 with: st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y} rounded 4 decimal places:", np.round(intrinsic_reward,4)) | st.dataframe(df_0_0[1:2])

Updated line 55 with: def calculate_intrinsic_reward(self, eta, count, epsilon):

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])

Updated line 4 with: pandas

Updated line 37 with: r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{1 + 1 x 10^{-5}}}

Updated lines 24-41 with: df_0_0 = pd.read_csv('df_0_0.csv') st.write(df_0_0.shape) # 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 formulat 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 times 10^{-5}}} """) st.write(f"Calculated intrinsic reward: {r_t_int}") st.dataframe(df_0_0[:1]) Updated line 2 with: import pandas as pd

Updated line 18 with: st.image("intrinsic_reward_formulation.png", caption='Intrinsic Reward Formulation')

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