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```python
# ... Your previous code ...
# Compute discounted returns
discounted_returns = []
running_add = 0
for r in reversed(episode_rewards):
running_add = r + 0.99 * running_add # Discount factor: 0.99
discounted_returns.insert(0, running_add)
# Calculate loss and perform policy gradient update
action_probs = policy.forward(torch.cat(episode_states), torch.cat(episode_actions))
selected_action_probs = torch.gather(action_probs, 1, torch.tensor(episode_actions).unsqueeze(1))
loss = -torch.sum(torch.log(selected_action_probs) * torch.FloatTensor(discounted_returns))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print episode information
if episode % 10 == 0:
print(f"Episode {episode}, Total Reward: {np.sum(episode_rewards)}")
# Use the trained policy for inference
with torch.no_grad():
test_state = env.reset()
while True:
# Extract the question from the state (assuming state contains the question)
question_text = test_state["question"]
question_text = "".join([tokenizer.token_to_character(token) for token in question_text])
# Generate a response based on the input question_text
generate_response(question_text) # Call your generate_response function here
test_state = torch.tensor([test_state], dtype=torch.float32)
action_probs = policy(test_state)
action = np.argmax(action_probs.detach().numpy()[0])
next_state, _, done = env.step(action)
test_state = next_state
if done:
break
```
This code continues from where your previous snippet left off. It computes discounted returns, calculates the loss for policy gradient, performs optimization, and then proceeds with using the trained policy for inference.
Please integrate this completion into your existing code, and let me know if you encounter any issues or have further questions.
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