Calculate env rollout

def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -> Rollout:
        """ Calculate env rollout """
        assert not model.is_recurrent, "Replay env roller does not support recurrent models"

        accumulator = TensorAccumulator()
        episode_information = []  # List of dictionaries with episode information

        for step_idx in range(number_of_steps):
            step = model.step(self.last_observation)

            replay_extra_information = {}

            accumulator.add('observations', self.last_observation_cpu)

            # Add step to the tensor accumulator
            for name, tensor in step.items():
                tensor_cpu = tensor.cpu()
                accumulator.add(name, tensor_cpu)

                if name != 'actions':
                    replay_extra_information[name] = tensor_cpu.numpy()

            actions_numpy = step['actions'].detach().cpu().numpy()
            new_obs, new_rewards, new_dones, new_infos = self.environment.step(actions_numpy)

            # Store rollout in the experience replay buffer
            self.replay_buffer.store_transition(
                frame=self.last_observation_cpu.numpy(),
                action=actions_numpy,
                reward=new_rewards,
                done=new_dones,
                extra_info=replay_extra_information
            )

            # Done is flagged true when the episode has ended AND the frame we see is already a first frame from the
            # next episode

            dones_tensor = torch.from_numpy(new_dones.astype(np.float32)).clone()
            accumulator.add('dones', dones_tensor)

            self.last_observation_cpu = torch.from_numpy(new_obs).clone()
            self.last_observation = self.last_observation_cpu.to(self.device)
            accumulator.add('rewards', torch.from_numpy(new_rewards.astype(np.float32)).clone())

            episode_information.append(new_infos)

        accumulated_tensors = accumulator.result()

        return Trajectories(
            num_steps=accumulated_tensors['observations'].size(0),
            num_envs=accumulated_tensors['observations'].size(1),
            environment_information=episode_information,
            transition_tensors=accumulated_tensors,
            rollout_tensors={
                'final_values': model.value(self.last_observation).cpu()
            }
        )