Notes

The error you're encountering suggests that the LSTM layers in your `PolicyNetwork` expect input tensors of a specific shape, but the tensors you provided have a shape that doesn't match the expected input shape.

In your `PolicyNetwork`, you are using `nn.LSTM` layers. LSTM layers expect input tensors with dimensions `(sequence_length, batch_size, input_size)`, where:

- `sequence_length` is the length of the input sequence (the number of time steps).
- `batch_size` is the number of sequences you're processing in parallel.
- `input_size` is the dimension of each element in the sequence.

Before passing the tensors to the LSTM layers, you need to ensure that they have the correct shape. You can do this by adjusting the dimensions accordingly.

Here's how you can modify the code to reshape the tensors before passing them to the `policy.forward` method:

```python
# Assuming sequence_length = 1, batch_size = len(episode_states), and input_size = your_input_size
# Reshape the tensors
question_tensors = question_tensors.view(1, len(episode_states), your_input_size)
correct_response_tensors = correct_response_tensors.view(1, len(episode_states), your_input_size)
incorrect_response_tensors = incorrect_response_tensors.view(1, len(episode_states), your_input_size)

# Forward pass through the policy network
action_probs = policy.forward(question_tensors, correct_response_tensors, incorrect_response_tensors)
```

Make sure to replace `your_input_size` with the appropriate input size for your model. This code reshapes the tensors to match the expected input shape for the LSTM layers.