Rename homer_plotly._visualizations_1.webp to homer_plotly_visualizations_1.webp

Updated lines 297, 500 with: st.image("homer_plotly_visualizations_1.webp", caption="Homer Simpson Meta-Learning HRL Lecture Plotly 1", use_column_width=True) | st.image("homer_plotly_visualizations_2.webp", caption="Homer Simpson Meta-Learning HRL Lecture Plotly 2", use_column_width=True)

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Update app.py

Updated lines 463-494 with: # Clean the State_2D column to remove any extra characters or spaces df['State_2D'] = df['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns df[['x', 'y']] = df['State_2D'].str.split(',', expand=True).astype(int) # 1. Compare State_2D and Extrinsic Reward fig1 = px.bar(df, x='State_2D', y='Extrinsic Reward', title='Comparison of State_2D and Extrinsic Reward', labels={'State_2D': 'State 2D', 'Extrinsic Reward': 'Extrinsic Reward'}) # 2. Compare Intrinsic Reward | Extrinsic Reward | State_2D fig2 = px.scatter(df, x='Intrinsic Reward', y='Extrinsic Reward', color='State_2D', title='Comparison of Intrinsic Reward, Extrinsic Reward, and State_2D', labels={'Intrinsic Reward': 'Intrinsic Reward', 'Extrinsic Reward': 'Extrinsic Reward', 'State_2D': 'State 2D'}) # 3. Compare Total Reward | Intrinsic Reward | Extrinsic Reward | State_2D fig3 = px.scatter_3d(df, x='Total Reward', y='Intrinsic Reward', z='Extrinsic Reward', color='State_2D', title='Comparison of Total Reward, Intrinsic Reward, Extrinsic Reward, and State_2D', labels={'Total Reward': 'Total Reward', 'Intrinsic Reward': 'Intrinsic Reward', 'Extrinsic Reward': 'Extrinsic Reward', 'State_2D': 'State 2D'}) # 4. Compare State_2D and Total Reward fig4 = px.bar(df, x='State_2D', y='Total Reward', title='Comparison of State_2D and Total Reward', labels={'State_2D': 'State 2D', 'Total Reward': 'Total Reward'}) # Display the visualizations using Streamlit st.title('Intrinsic Analysis Visualizations') st.plotly_chart(fig1) st.plotly_chart(fig2) st.plotly_chart(fig3) st.plotly_chart(fig4)

Updated line 434 with: visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True)

Updated line 444 with: [hashtag removal] for })

Updated lines 430-460 with: visitation_counts = df['State_2D'].value_counts().reset_index() visitation_counts.columns = ['State_2D', 'Visitation_Count'] # Clean the State_2D column to remove any extra characters or spaces#visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Create a 6x6 grid with zero visitation counts for all positions grid_size = 6 heatmap_data = pd.DataFrame({ 'x': [x for x in range(grid_size) for y in range(grid_size)], 'y': [y for x in range(grid_size) for y in range(grid_size)], 'Visitation_Count': 0 #}) # Merge the visitation counts with the grid data heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0) # Create the Plotly heatmap fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'}, nbinsx=grid_size, nbinsy=grid_size) # Reverse the y-axis to display 0 to 5 from top to bottom fig.update_yaxes(autorange="reversed") # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig)

Updated lines 386-421 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'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Invert the y-coordinates to match the desired orientation grid_size = 6 visitation_counts['y'] = grid_size - 1 - visitation_counts['y'] # Create a 6x6 grid with zero visitation counts for all positions heatmap_data = pd.DataFrame({ 'x': [x for x in range(grid_size) for y in range(grid_size)], 'y': [y for x in range(grid_size) for y in range(grid_size)], 'Visitation_Count': 0 }) # Merge the visitation counts with the grid data heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0) # Create the Plotly heatmap fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'}, nbinsx=grid_size, nbinsy=grid_size) # Reverse the y-axis to display 0 to 5 from top to bottom fig.update_yaxes(autorange="reversed") # Display the heatmap using Streamlit with a unique key st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig, key='unique_diamond')

st.plotly_chart(fig, key='this_one_is_unique')

Updated lines 348-382 with: # Visualization with the origin initializing at the top-left corner # Goal tiles should appear on the bottom right at two sets of coordinates located at (4,5) and (5,4) # 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'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Invert the y-coordinates to match the desired orientation grid_size = 6 visitation_counts['y'] = grid_size - 1 - visitation_counts['y'] # Create a 6x6 grid with zero visitation counts for all positions heatmap_data = pd.DataFrame({ 'x': [x for x in range(grid_size) for y in range(grid_size)], 'y': [y for x in range(grid_size) for y in range(grid_size)], 'Visitation_Count': 0 }) # Merge the visitation counts with the grid data heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0) # Create the Plotly heatmap fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'}, nbinsx=grid_size, nbinsy=grid_size) # Display the heatmap using Streamlit with a unique key st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig, key='unique_heatmap_key')

Updated lines 265-281 with: [hashtag notation]

Updated line 311 with: st.plotly_chart(fig, key='unique_heatmap_key')

Updated lines 290-292 with: df = pd.read_csv('goal_rows.csv') st.write('Goal Rows DataFrame:') st.dataframe(df)

Updated lines 308, 310-339 with: st.plotly(fig) | # 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'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Create a 6x6 grid with zero visitation counts for all positions grid_size = 6 heatmap_data = pd.DataFrame({ 'x': [x for x in range(grid_size) for y in range(grid_size)], 'y': [y for x in range(grid_size) for y in range(grid_size)], 'Visitation_Count': 0 }) # Merge the visitation counts with the grid data heatmap_data = heatmap_data.merge(visitation_counts, on=['x', 'y'], how='left').fillna(0) # Create the Plotly heatmap fig = px.density_heatmap(heatmap_data, x='x', y='y', z='Visitation_Count_y', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count_y': 'Visitation Count'}, nbinsx=grid_size, nbinsy=grid_size) # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig)

Update app.py

Updated lines 263, 287-308 with: [notation] # Visualization only includes the (4,5) and (5,4) 6 x 6 Grid Map Positions | # Visualization leveraging intrinsic_analysis.csv includes the entire (6x6) Grid-World (Map Positions) # Load the CSV data df = pd.read_csv('intrinsic_analysis.csv') # 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'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Create the Plotly heatmap fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count': 'Visitation Count'}) # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig)

Update app.py

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Updated line 194 with: st.image("homer_formula_7035_2.webp", caption='Homer Meta-Learning HRL Formula 7035 2')

Upload homer_formula_7035.webp

Updated line 193 with: st.image("homer_formula_7035.webp", caption='Homer Meta-Learning HRL Formula 7035')

Updated line 151 with: st.audio("h3.wav", format="audio/wav")

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Updated line 154 with: st.write("Last Example 7035 via Method 2:")

Updated line 185 with: st.write(f"Intrinsic Reward @ {count} @ Coordinates {x,y} rounded 4 decimal places:", np.round(intrinsic_reward,4))

Updated lines 153-189 with: # Display the formulation with parameters plugged in st.write("Last Example in df_0_0 DataFrame 7035 via Method 2:") st.latex(r""" r_{t}^{int} \eta \frac{1}{\sqrt{N(s_{t}) + \epsilon}} = 0.1 \frac{1}{\sqrt{7035 + 1(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 = 7035 epsilon = 1e-5 x,y = 0,0 # Create instance for Intrinsic Reward Calculation irc = ConcreteIntrinsicRewardCalculator() intrinsic_reward = irc.calculate_intrinsic_reward(0.1, 7035, 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[7034:7035])

Updated 36, 46, 90, 101, 145 with: 0.1 \frac{1}{\sqrt{1 + 1(10^{-5})}} | 0.1 \frac{1}{\sqrt{2 + 1(10^{-5})}} | 0.1 \frac{1}{\sqrt{3 + 1(10^{-5})}} | 0.1 \frac{1}{\sqrt{4 + 1(10^{-5})}} | 0.1 \frac{1}{\sqrt{5 + 1(10^{-5})}}

Updated lines 222-239 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'] # Clean the State_2D column to remove any extra characters or spaces visitation_counts['State_2D'] = visitation_counts['State_2D'].str.replace(r'[^\d,]', '', regex=True) # Split the cleaned State_2D into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Create the Plotly heatmap fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count': 'Visitation Count'}) # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig)

Updated lines 222-240 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'] # Assuming State_2D is a 2D coordinate (e.g., 'x,y'), split it into separate columns visitation_counts[['x', 'y']] = visitation_counts['State_2D'].str.split(',', expand=True).astype(int) # Create the Plotly heatmap fig = px.density_heatmap(visitation_counts, x='x', y='y', z='Visitation_Count', title='Goal Position Visitation Counts Heatmap', labels={'x': 'X Coordinate', 'y': 'Y Coordinate', 'Visitation_Count': 'Visitation Count'}) # Display the heatmap using Streamlit st.title('Goal Position Visitation Counts Heatmap Visualization') st.plotly_chart(fig) df = pd.read_csv('intrinsic_analysis.csv') st.write("Intrinsic Analysis DataFrame:") st.dataframe(df)

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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