Notes

I apologize for the oversight. To utilize both the correct and incorrect responses from your data, you can modify the `CustomEnv` class and the `step()` method to incorporate the incorrect response as an additional state variable. Here's how you can do it:

```python
import gym
from gym import spaces
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, questions, correct_responses, incorrect_responses):
super(CustomEnv, self).__init__()

# Store your data
self.questions = questions
self.correct_responses = correct_responses
self.incorrect_responses = incorrect_responses
self.num_questions = len(questions)

# 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
new_response = your_language_model.generate_response(self.questions[question_idx])

# Calculate the rewards based on similarity metrics
similarity_correct = similarity_metric.calculate_similarity(new_response, self.correct_responses[question_idx])
similarity_incorrect = similarity_metric.calculate_similarity(new_response, self.incorrect_responses[question_idx])

# 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

# Initialize the environment with your data
questions = [...] # List of questions
correct_responses = [...] # List of correct responses corresponding to questions
incorrect_responses = [...] # List of incorrect responses corresponding to questions
env = CustomEnv(questions, correct_responses, incorrect_responses)

# Training loop for your RL agent
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

# Agent updates its policy based on the reward (RL algorithm-specific)

# Evaluate the trained agent and your language model
```

In this modified code, I added `incorrect_responses` as an input to the `CustomEnv` class, and the state space now includes `incorrect_response_idx`. The reward calculation now takes into account the similarity between the new response, correct response, and incorrect response, allowing the agent to learn to generate responses closer to the correct ones and away from the incorrect ones.

Again, please note that this code provides a high-level structure and may require additional customization depending on your specific language model, RL algorithm, and problem domain.