Comments (1)
Changing the order of codes (.backward(), .step()) as below solved the error (torch 1.8.0+cu111).
In my knowledge, the error might be raised because the action_loss value can not be backpropagated after the "value_optimizer.step()" is run.
# update_value model
value_loss = 0.5 * mse_loss(imaginated_values, lambda_target_values.detach())
value_optimizer.zero_grad()
clip_grad_norm_(value_model.parameters(), args.clip_grad_norm)
# update action model (multiply -1 for gradient ascent)
action_loss = -1 * (lambda_target_values.mean())
action_optimizer.zero_grad()
clip_grad_norm_(action_model.parameters(), args.clip_grad_norm)
value_loss.backward(retain_graph=True)
action_loss.backward()
value_optimizer.step()
action_optimizer.step()
from dreamer_pytorch.
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from dreamer_pytorch.