OpenAI's REINFORCE and actor-critic example for reinforcement learning has the following code: REINFORCE: policy_loss = torch.cat(policy_loss).sum() actor-critic: loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum() One is using torch.cat, the other uses torch.stack, for similar use cases. As far as my understanding goes, the doc doesn't give any clear distinction between them. I would be happy to know the differences between the functions. Continue reading...
