Notes

Sure, I can help you modify the code to work with your data in a DataFrame. You'll need to use the Pandas library to handle the DataFrame. Here's an example of how you can adapt the code:

```python
import gym
from gym import spaces
import random
import pandas as pd # Import Pandas to handle the DataFrame
import your_language_model # Import your language model implementation
import similarity_metric # Import a similarity metric (e.g., BLEU or ROUGE)

class CustomEnv(gym.Env):
def __init__(self, data_df):
super(CustomEnv, self).__init__()

# Store your data DataFrame
self.data_df = data_df
self.num_questions = len(data_df)

# Define your state space (question, current_response, incorrect_response)
self.state_space = spaces.Tuple((spaces.Discrete(self.num_questions), spaces.Discrete(1), spaces.Discrete(1)))

# Define your action space (e.g., generate a new response)
self.action_space = spaces.Discrete(1) # You can customize this based on your actions

def step(self, action):
# Get the current state (question_idx, current_response_idx, incorrect_response_idx)
question_idx, current_response_idx, incorrect_response_idx = self.state

# Generate a new response using your language model based on the current question
current_row = self.data_df.iloc[question_idx]
new_response = your_language_model.generate_response(current_row['question'])

# Calculate the rewards based on similarity metrics
similarity_correct = similarity_metric.calculate_similarity(new_response, current_row['correct_response'])
similarity_incorrect = similarity_metric.calculate_similarity(new_response, current_row['incorrect_response'])

# Calculate the reward as the difference between similarity to correct and similarity to incorrect response
reward = similarity_correct - similarity_incorrect

# Update the state with the new response
self.state = (question_idx, new_response, incorrect_response_idx)

# Determine if the episode is done (e.g., after a fixed number of steps)
done = (current_response_idx == max_steps)

return self.state, reward, done, {}

def reset(self):
# Reset the environment to a new episode (e.g., choose a new question and incorrect response)
self.state = (random.randint(0, self.num_questions - 1), 0, 0)

def render(self):
# Optional: Implement a method to visualize the environment


# Load your data into a DataFrame
data = pd.DataFrame({
'question': [...], # List of questions
'correct_response': [...], # List of correct responses corresponding to questions
'incorrect_response': [...] # List of incorrect responses corresponding to questions
})

# Initialize the environment with your data
env = CustomEnv(data)

# Training loop for your RL agent
num_episodes = ...
max_steps = ...

for episode in range(num_episodes):
state = env.reset()
done = False
total_reward = 0

while not done:
action = agent.select_action(state)
next_state, reward, done, _ = env.step(action)
total_reward += reward
```

In this modified code, I assume that you have loaded your data into a DataFrame called `data`, with columns 'question,' 'correct_response,' and 'incorrect_response.' The environment is then initialized using this DataFrame. The rest of the code remains similar to your original implementation.