if self.arch == "mlp":
                    if self.accelerator is not None:
                        with actor.no_sync():
                            action = actor(states[agent_id])
                    else:
                        action = actor(states[agent_id])
                    if not self.discrete_actions:
                        action = torch.where(
                            action > 0,
                            action * self.max_action[idx][0],
                            action * -self.min_action[idx][0],
                        )
                    detached_actions = copy.deepcopy(actions)
                   detached_actions[agent_id] = action
                    input_combined = torch.cat(
                        list(states.values())   list(detached_actions.values()), 1
                    )
                    if self.accelerator is not None:
                        with critic_1.no_sync():
                            actor_loss = -critic_1(input_combined).mean()
                    else:
                        actor_loss = -critic_1(input_combined).mean()```
**detached_actions[agent_id] = action ** I don't quite understand this code Why does it update the action in the current loop individually instead of updating all agent actions?

I asked claude and the answer he gave was: This ensures that the actions of all other agents except the current agent are detached (separated from the calculation graph) and will not affect the calculation graph and updates. I still don't quite understand

This code is located at line 689 of the agilerl library matd3 file