app.py

import streamlit as st
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
st.title("Homer Simpson Meta-Learning with Hierarchical Reinforcement Learning Intrinsic Reward Lecture")

# Display the image with a caption
st.image("homer.webp", caption="Homer Simpson Meta-Learning HRL Lecture", use_column_width=True)

# Display and play the audio files
st.write("Audio Playback Meta-Learning with HRL Intrinsic Reward Lecture:")
st.audio("h0.wav", format="audio/wav")
st.audio("h1.wav", format="audio/wav")
st.write("Oh, sweet Homer's doughnuts! If that second .wav file ain't playin', just download the darn thing! Mmm... downloads...")
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):")

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.write(f"Calculated intrinsic reward rounded 2 decimal places:", np.round(r_t_int,2))
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 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.write(f"Calculated intrinsic reward rounded 4 decimal places:", np.round(r_t_int,4))
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])

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!")



# Define the grid and visitations
grid = np.zeros((6, 6))
visitations = {
    (0, 0): 7035, (1, 0): 3579, (2, 0): 1359, (2, 1): 1707, (3, 1): 520, (4, 1): 227,
    (4, 2): 243, (5, 1): 217, (5, 2): 181, (5, 0): 241, (4, 0): 267, (5, 3): 179,
    (4, 3): 1034, (3, 3): 2163, (2, 3): 2080, (0, 1): 3313, (1, 1): 3015, (0, 2): 1846,
    (0, 3): 1104, (0, 4): 351, (1, 4): 518, (1, 3): 1497, (1, 2): 2236, (2, 2): 2239,
    (2, 4): 842, (1, 5): 238, (2, 5): 217, (0, 5): 341, (3, 5): 382, (4, 5): 1872,
    (4, 4): 2038, (3, 4): 1684, (3, 0): 383, (3, 2): 1102, (5, 4): 198
}

# Fill the grid with visitations
for (x, y), count in visitations.items():
    grid[x, y] = count

# Calculate the total number of visitations
total_visitations = sum(visitations.values())

# Calculate the percentages
percentages = {state: (count / total_visitations) * 100 for state, count in visitations.items()}

# Print the percentages in the specified order
order = [
    (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
    (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
    (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5),
    (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (3, 5),
    (4, 0), (4, 1), (4, 2), (4, 3), (4, 4), (4, 5),
    (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (5, 5)
]

st.title("State Visitations Visualization")

st.write("### State Visitations Percentages:")
for state in order:
    st.write(f"State {state}: {percentages.get(state, 0):.2f}%")

# Create a pie chart
labels = [f"State {state}" for state in visitations.keys()]
values = list(visitations.values())

fig_pie = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig_pie.update_layout(title_text="State Visitations Pie Chart")
st.plotly_chart(fig_pie)

# Create a heatmap
fig_heatmap = px.imshow(grid, labels=dict(x="Column", y="Row", color="Visitations"),
x=list(range(6)), y=list(range(6)), title="State Visitations Heatmap")
fig_heatmap.update_xaxes(side="top")
st.plotly_chart(fig_heatmap)

# Load the CSV data
df = pd.read_csv('goal_rows.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']

# 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)