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...")

Upload h1.wav

Delete h1.wav

Upload 2 files

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

Upload 3 files

Delete goal_rows.csv

Delete df_0_0.csv

Delete intrinsic_analysis.csv

Upload 3 files

Update requirements.txt

Update app.py

Create dataframes.txt

Create algo2.txt

Upload h0.wav

Upload Algo2_Meta_Learning.png

Create app.py

init!

Upload 2410.07921v1.pdf

Upload intrinsic_reward_formulation.png

Upload homer.webp

initial commit