Updated lines 209-220 with: # Aggregate the data to count the number of visits to each State_2D visitation_counts = df['State_2D'].value_counts().reset_index() visitation_counts.columns = ['State_2D', 'Visitation_Count'] # Create the Plotly bar chart fig = px.bar(visitation_counts, x='State_2D', y='Visitation_Count', title='Goal Position Visitation Counts', labels={'State_2D': 'State 2D', 'Visitation_Count': 'Visitation Count'}) # Display the plot using Streamlit st.title('Goal Position Visitation Counts Visualization') st.plotly_chart(fig)

Updated line 207 with: df = pd.read_csv('goal_rows.csv')

Updated lines 200-221 with: # Load the CSV data df = pd.read_csv('goals_rows.csv') # Assuming the CSV has columns 'goal_position' and 'visitation_count' # If the column names are different, adjust the following lines accordingly goal_positions = df['goal_position'] visitation_counts = df['visitation_count'] # Create the Plotly bar chart fig = px.bar(df, x='goal_position', y='visitation_count', title='Goal Position Visitation Counts', labels={'goal_position': 'Goal Position', 'visitation_count': 'Visitation Count'}) # Display the plot using Streamlit st.title('Goal Position Visitation Counts Visualization') st.plotly_chart(fig)

Updated line 151 with: st.write("Oh, sweet Krusty-licious! At coordinates (0,0) for that plotly visualization, we need a whopping 7035 intrinsic reward calculations to get things rollin'! And don't forget to update those State Visitations. Those were just the first five. Mmm... 7030 more to go... D'oh!")

Updated line 151 with: st.write("Oh, sweet Krusty-licious! At coordinates (0,0) for that plotly visualization, we need a whopping 7305 intrinsic reward calculations to get things rollin'! And don't forget to update those State Visitations. Those were just the first five. Mmm... 7300 more to go... D'oh!")

Updated line 151 with: st.write('Oh, sweet Krusty-licious! At coordinates (0,0) for that plotly visualization, we need a whopping 7305 intrinsic reward calculations to get things rollin'! And don't forget to update those State Visitations. Those were just the first five. Mmm... 7300 more to go... D'oh!')

Updated line 39 with: st.write(f"Calculated intrinsic reward rounded 2 decimal places:", np.round(r_t_int,2))

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')

Updated line 16 with: st.write("Oh, sweet Homer's doughnuts! If that second .wav file ain't playin', just download the darn thing! Mmm... downloads...")

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Updated lines 15-16 with: st.audio("h1.wav", format="audio/wav") st.audio("h2.wav", format="audio/wav")

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