code: def run(self):
          
          device = self.model_config.torch_device()
  
          # Reinforcer is the learner for the reinforcement learning model
          reinforcer = self.reinforcer.instantiate(device)
          optimizer = self.optimizer_factory.instantiate(reinforcer.model)
  
          # All callbacks used for learning
          callbacks = self.gather_callbacks(optimizer)
          # Metrics to track through this training
          metrics = reinforcer.metrics()
  
          training_info = self.resume_training(reinforcer, callbacks, metrics)
  
          reinforcer.initialize_training(training_info)
          training_info.on_train_begin()
  
          if training_info.optimizer_initial_state:
              optimizer.load_state_dict(training_info.optimizer_initial_state)
  
          global_epoch_idx = training_info.start_epoch_idx + 1
  
          while training_info['frames'] < self.total_frames:
              epoch_info = EpochInfo(
                  training_info,
                  global_epoch_idx=global_epoch_idx,
                  batches_per_epoch=self.batches_per_epoch,
                  optimizer=optimizer,
              )
  
              reinforcer.train_epoch(epoch_info)
  
              if self.openai_logging:
                  self._openai_logging(epoch_info.result)
  
              self.storage.checkpoint(epoch_info, reinforcer.model)
  
              global_epoch_idx += 1
  
          training_info.on_train_end()
  
          return training_info
docs: Run reinforcement learning algorithm